
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import sweetviz as sv
from category_encoders.hashing import HashingEncoder


data = pd.read_csv('E:\IUST\ترم 8\ایمنی و بهداشت صنعتی\پروژه\حوادث و آسیب های کارکنان ساختمانی\OSHA accidents and injuries of construction workers\\OSHA HSE DATA.csv')


data.head()


data.tail()


data.sample(5)


data.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 4847 entries, 0 to 4846
Data columns (total 29 columns):
 #   Column                Non-Null Count  Dtype 
---  ------                --------------  ----- 
 0   summary_nr            4847 non-null   int64 
 1   Event Date            4847 non-null   object
 2   Abstract Text         4847 non-null   object
 3   Event Description     4847 non-null   object
 4   Event Keywords        4847 non-null   object
 5   con_end               4847 non-null   object
 6   Construction End Use  4847 non-null   object
 7   build_stor            4847 non-null   int64 
 8   Building Stories      4847 non-null   object
 9   proj_cost             4847 non-null   object
 10  Project Cost          4847 non-null   object
 11  proj_type             4847 non-null   object
 12  Project Type          4847 non-null   object
 13  Degree of Injury      4847 non-null   object
 14  nature_of_inj         4847 non-null   int64 
 15  Nature of Injury      4845 non-null   object
 16  part_of_body          4847 non-null   int64 
 17  Part of Body          4845 non-null   object
 18  event_type            4847 non-null   int64 
 19  Event type            4845 non-null   object
 20  evn_factor            4847 non-null   int64 
 21  Environmental Factor  4840 non-null   object
 22  hum_factor            4847 non-null   int64 
 23  Human Factor          4840 non-null   object
 24  task_assigned         4847 non-null   int64 
 25  Task Assigned         4847 non-null   object
 26  hazsub                4847 non-null   object
 27  fat_cause             4847 non-null   int64 
 28  fall_ht               4847 non-null   int64 
dtypes: int64(10), object(19)
memory usage: 1.1+ MB


data.describe()


data.columns

Index(['summary_nr', 'Event Date', 'Abstract Text', 'Event Description',
       'Event Keywords', 'con_end', 'Construction End Use', 'build_stor',
       'Building Stories', 'proj_cost', 'Project Cost', 'proj_type',
       'Project Type', 'Degree of Injury', 'nature_of_inj', 'Nature of Injury',
       'part_of_body', 'Part of Body', 'event_type', 'Event type',
       'evn_factor', 'Environmental Factor', 'hum_factor', 'Human Factor',
       'task_assigned', 'Task Assigned', 'hazsub', 'fat_cause', 'fall_ht'],
      dtype='object')


data['summary_nr'].nunique()

4844


data[data['summary_nr'].duplicated()]


data[data['summary_nr']==220957740]


data.drop(63,axis=0,inplace=True)


data[data['summary_nr']==220957740]


data[data['summary_nr']==220873897]


data.drop([3081,3082],axis=0,inplace=True)


data[data['summary_nr']==220873897]


data['summary_nr'].nunique()

4844


data.duplicated()

0       False
1       False
2       False
3       False
4       False
        ...  
4842    False
4843    False
4844    False
4845    False
4846    False
Length: 4844, dtype: bool


data.duplicated().sum()

0


type(data['Event Date'].iloc[0])

str


# M / D / Y
data['Event Date'].head(1)

0    8/10/2017
Name: Event Date, dtype: object


data['Event Date'] = data['Event Date'].apply(lambda x:pd.to_datetime(x))


data['Event Date'].head()

0   2017-08-10
1   2017-07-17
2   2017-06-30
3   2017-06-30
4   2017-06-23
Name: Event Date, dtype: datetime64[ns]


type(data['Event Date'].iloc[0])

pandas._libs.tslibs.timestamps.Timestamp


data['Event DayOfWeek'] = data['Event Date'].apply(lambda x:x.dayofweek)


data['Event Day'] = data['Event Date'].apply(lambda x:x.day)


data['Event Month'] = data['Event Date'].apply(lambda x:x.month)


data['Event Year'] = data['Event Date'].apply(lambda x:x.year)


dayofweek_map = {0:'Mon',1:'Tue',2:'Wed',3:'Thu',4:'Fri',5:'Sat',6:'Sun'}
data['Event DayOfWeek'] = data['Event DayOfWeek'].map(dayofweek_map)


data


plt.figure(figsize=(8,5))
sns.heatmap(data=data.isnull(),cbar=False,yticklabels=False,cmap='viridis')

<AxesSubplot:>


data.isnull().sum()

summary_nr              0
Event Date              0
Abstract Text           0
Event Description       0
Event Keywords          0
con_end                 0
Construction End Use    0
build_stor              0
Building Stories        0
proj_cost               0
Project Cost            0
proj_type               0
Project Type            0
Degree of Injury        0
nature_of_inj           0
Nature of Injury        2
part_of_body            0
Part of Body            2
event_type              0
Event type              2
evn_factor              0
Environmental Factor    7
hum_factor              0
Human Factor            7
task_assigned           0
Task Assigned           0
hazsub                  0
fat_cause               0
fall_ht                 0
Event DayOfWeek         0
Event Day               0
Event Month             0
Event Year              0
dtype: int64


data.dropna(inplace=True)


plt.figure(figsize=(8,5))
sns.heatmap(data=data.isnull(),cbar=False,yticklabels=False,cmap='viridis')

<AxesSubplot:>


data['Abstract Text'].iloc[0]

'At 9:00 a.m. on August 10, 2017, an employee was operating a 400 ton Bliss Coin "Knuckle" mechanical power press. The press was actuated while the employee\'s right hand was in the point of operation. The employee\'s right ring and middle fingers were amputated. Coin "Knuckle" mechanical power press. The press was actuated while the employee\'s right hand was in the point of operation. The employee\'s right ring and middle fingers were amputated. '


data['Abstract Text'].iloc[4830]

'At approximately 9:15 a.m. on July 1, 2015, Employee #1 was trouble shooting lights at the top of a billboard. He was climbing down a ladder from the top walkway to the lower walkway, when he lost his balance and fell for some unknown reason. Employee #1 fell 40 ft and was killed. He struck the ground and sustained severe head and internal injuries. '


def parse_time(x):
    words = x.split(' ')
    string = ''
    for word in words:
        if ':' in word:
            string += word
        if '.m.' in word:
            string += f' {word}'
    if 11 > len(string) > 8:
        return string


data['Time'] = data['Abstract Text'].apply(parse_time)


data['Time']

0        9:00 a.m.
1        9:45 a.m.
2        7:30 a.m.
3        2:00 p.m.
4       12:20 p.m.
           ...    
4842     9:15 a.m.
4843     8:30 a.m.
4844     3:00 p.m.
4845     2:30 p.m.
4846     2:15 p.m.
Name: Time, Length: 4835, dtype: object


data['Time'].isna().sum()

467


def clock_format(y):
    if y==None:
        pass
    else:
        words = y.split(' ')
        string = ''
        for word in words:
            if 'a.m.' in word:
                hour = words[0]
                return hour
            elif 'p.m.' in word:
                list1 = words[0].split(':')
                hour = list1[0]
                hour1=int(hour)+12
                minute = list1[1] 
                hour2 = str(hour1)
                minute1 = str(minute)
                if hour1 == 24:
                    hour2 = hour1-12
                    hour2 = str(hour2)
                return hour2+':'+minute1


data['Time'] = data['Time'].apply(clock_format)


data['Time']

0        9:00
1        9:45
2        7:30
3       14:00
4       12:20
        ...  
4842     9:15
4843     8:30
4844    15:00
4845    14:30
4846    14:15
Name: Time, Length: 4835, dtype: object


data.dropna(inplace=True)


data.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 4367 entries, 0 to 4846
Data columns (total 34 columns):
 #   Column                Non-Null Count  Dtype         
---  ------                --------------  -----         
 0   summary_nr            4367 non-null   int64         
 1   Event Date            4367 non-null   datetime64[ns]
 2   Abstract Text         4367 non-null   object        
 3   Event Description     4367 non-null   object        
 4   Event Keywords        4367 non-null   object        
 5   con_end               4367 non-null   object        
 6   Construction End Use  4367 non-null   object        
 7   build_stor            4367 non-null   int64         
 8   Building Stories      4367 non-null   object        
 9   proj_cost             4367 non-null   object        
 10  Project Cost          4367 non-null   object        
 11  proj_type             4367 non-null   object        
 12  Project Type          4367 non-null   object        
 13  Degree of Injury      4367 non-null   object        
 14  nature_of_inj         4367 non-null   int64         
 15  Nature of Injury      4367 non-null   object        
 16  part_of_body          4367 non-null   int64         
 17  Part of Body          4367 non-null   object        
 18  event_type            4367 non-null   int64         
 19  Event type            4367 non-null   object        
 20  evn_factor            4367 non-null   int64         
 21  Environmental Factor  4367 non-null   object        
 22  hum_factor            4367 non-null   int64         
 23  Human Factor          4367 non-null   object        
 24  task_assigned         4367 non-null   int64         
 25  Task Assigned         4367 non-null   object        
 26  hazsub                4367 non-null   object        
 27  fat_cause             4367 non-null   int64         
 28  fall_ht               4367 non-null   int64         
 29  Event DayOfWeek       4367 non-null   object        
 30  Event Day             4367 non-null   int64         
 31  Event Month           4367 non-null   int64         
 32  Event Year            4367 non-null   int64         
 33  Time                  4367 non-null   object        
dtypes: datetime64[ns](1), int64(13), object(20)
memory usage: 1.2+ MB


data['Time'] = data['Time'].apply(lambda x:pd.to_datetime(x))


data['Time']

0      2023-05-25 09:00:00
1      2023-05-25 09:45:00
2      2023-05-25 07:30:00
3      2023-05-25 14:00:00
4      2023-05-25 12:20:00
               ...        
4842   2023-05-25 09:15:00
4843   2023-05-25 08:30:00
4844   2023-05-25 15:00:00
4845   2023-05-25 14:30:00
4846   2023-05-25 14:15:00
Name: Time, Length: 4367, dtype: datetime64[ns]


data['Hour'] = data['Time'].apply(lambda x:x.hour)


data['Minutes'] = data['Time'].apply(lambda x:x.minute)


data['Time'] = data['Time'].apply(lambda x:x.time())


data


data['Event Description'].iloc[0]

"EMPLOYEE'S FINGERS AMPUTATED WHILE OPERATING A 400 TON MECHA"


data['Event Description'].iloc[1]

"EMPLOYEE'S FINGER IS CAUGHT IN DRILL AND IS AMPUTATED"


data['Event Keywords'].iloc[0]

'FINGER,MECHANICAL POWER PRESS,AMPUTATION,GUARD'


data['Event Keywords'].iloc[1]

'CAUGHT IN,DRIVE SHAFT,RESIDENTIAL CONSTRUCTION,AMPUTATION,DRILL'


data['Event Keywords'].value_counts()

HEART ATTACK                                                       41
FALL,ROOF                                                          22
WORKPLACE VIOLENCE,GUNSHOT                                         18
FALL,LADDER                                                        17
HEART ATTACK,HEART                                                 16
                                                                   ..
RUPTURE,BURN,CHEST,NECK,FOOD PREPARATION,FACE                       1
ACCESS/EGRESS,BURN,FRYER,LEG                                        1
SLIPPERY SURFACE,FALL,NECK,LACERATION,STAIR                         1
CAUGHT BETWEEN,CRUSHED,DOOR                                         1
CRUSHING,STRUCK BY,POWERED INDUSTRIAL VEHICLE,MATERIAL HANDLING     1
Name: Event Keywords, Length: 4026, dtype: int64


data['Event Keywords'].apply(lambda x:x.lower().split(',')[0])

0                finger
1             caught in
2             amputated
3                   rib
4             struck by
             ...       
4842               fall
4843               fall
4844    unconsciousness
4845          struck by
4846           crushing
Name: Event Keywords, Length: 4367, dtype: object


data['con_end'].unique()

array(['0', 'H', 'C', 'G', 'B', 'I', 'M', 'A', 'D', 'O', 'Q', 'F', 'P',
       'J', 'K', 'L', 'N', 'E'], dtype=object)


data['con_end'].value_counts()

0    3408
C     262
A     176
H     131
B     119
I      82
D      36
P      32
M      29
G      20
J      19
O      16
Q       8
E       8
F       7
K       6
N       6
L       2
Name: con_end, dtype: int64


data['Construction End Use'].value_counts()

                                         3408
Commercial building                       262
Single family or duplex dwelling          176
Other building                            131
Multi-family dwelling                     119
Highway, road, street                      82
Manufacturing plant                        36
Other heavy construction                   32
Pipeline                                   29
Sewer/water treatment plant                20
Bridge                                     19
Power line, transmission line              16
Contractor's yard/facility                  8
Refinery                                    8
Power plant                                 7
Tower, tank, storage ,elevator              6
Excavation, landfill                        6
Shoreline development, dam, reservoir       2
Name: Construction End Use, dtype: int64


data['build_stor'].value_counts()

0      3718
1       268
2       190
3        74
4        35
5        25
6        13
7        12
10        7
15        3
11        3
8         3
13        2
40        2
19        2
9         2
76        1
16        1
139       1
46        1
12        1
14        1
21        1
25        1
Name: build_stor, dtype: int64


data[data['Building Stories']=='139'].loc[525]

summary_nr                                                      220945893
Event Date                                            2017-04-05 00:00:00
Abstract Text           At approximately 10:30 a.m. on April 5, 2017, ...
Event Description       EMPLOYEE  FALLS FROM VERTICAL LADDER AND IS KI...
Event Keywords          PLATFORM,FALL,LADDER,SCAFFOLD,FRACTURE,ELEVATE...
con_end                                                                 D
Construction End Use                                  Manufacturing plant
build_stor                                                            139
Building Stories                                                      139
proj_cost                                                               G
Project Cost                                   $10,000,000 to $20,000,000
proj_type                                                               A
Project Type                                  New project or new addition
Degree of Injury                                                    Fatal
nature_of_inj                                                           6
Nature of Injury                                              Head Trauma
part_of_body                                                           13
Part of Body                                                         Head
event_type                                                              5
Event type                                          Fall (from elevation)
evn_factor                                                             18
Environmental Factor                                                Other
hum_factor                                                             14
Human Factor                                                        Other
task_assigned                                                           1
Task Assigned                                          Regularly Assigned
hazsub                                                                  0
fat_cause                                                              16
fall_ht                                                                 0
Event DayOfWeek                                                       Wed
Event Day                                                               5
Event Month                                                             4
Event Year                                                           2017
Time                                                             10:30:00
Hour                                                                   10
Minutes                                                                30
Name: 525, dtype: object


data['Building Stories'].value_counts()

       3718
1       268
2       190
3        74
4        35
5        25
6        13
7        12
10        7
15        3
11        3
8         3
13        2
40        2
19        2
9         2
76        1
16        1
139       1
46        1
12        1
14        1
21        1
25        1
Name: Building Stories, dtype: int64


data['build_stor']

0       0
1       1
2       0
3       1
4       0
       ..
4842    0
4843    0
4844    0
4845    0
4846    0
Name: build_stor, Length: 4367, dtype: int64


data['Building Stories']

0        
1       1
2        
3       1
4        
       ..
4842     
4843     
4844     
4845     
4846     
Name: Building Stories, Length: 4367, dtype: object


data['Building Stories'].iloc[0]

' '


data['proj_cost'].value_counts()

0    3813
A     148
E      98
B      73
D      70
C      68
G      50
F      47
Name: proj_cost, dtype: int64


data['Project Cost'].value_counts()

                              3813
Under $50,000                  148
$1,000,000 to $5,000,000        98
$50,000 to $250,000             73
$500,000 to $1,000,000          70
$250,000 to $500,000            68
$10,000,000 to $20,000,000      50
$5,000,000 to $10,000,000       47
Name: Project Cost, dtype: int64


data.head()


data['proj_type'].value_counts()

0    3358
A     527
B     247
C     149
D      46
E      40
Name: proj_type, dtype: int64


data['Project Type'].value_counts()

                                3358
New project or new addition      527
Alteration or rehabilitation     247
Maintenance or repair            149
Demolition                        46
Other                             40
Name: Project Type, dtype: int64


data['Degree of Injury'].value_counts()

Fatal       2598
Nonfatal    1769
Name: Degree of Injury, dtype: int64


data['nature_of_inj'].value_counts()

21    1460
12     774
1      517
7      352
6      299
3      269
2      188
10     185
5      127
9       62
18      45
4       27
15      26
20      23
11       6
17       4
13       1
22       1
16       1
Name: nature_of_inj, dtype: int64


data['Nature of Injury'].value_counts()

Serious Fall/Strike       1460
Fracture, Broken Bones     774
Amputation, Crushing       517
Laceration                 352
Head Trauma                299
Bruising, Contusion        269
Asphyxiation, Drowning     188
Electrocution              185
Fire Burn                  127
Dislocation                 62
Puncture                    45
Chemical Burn               27
Heat Exhaustion             26
Fall/strike                 23
Eye injury                   6
Poison                       4
Freezer burn                 1
Fall from Elevation          1
Illness                      1
Name: Nature of Injury, dtype: int64


data['part_of_body'].value_counts()

13    918
10    524
4     493
19    354
31    318
5     272
16    249
12    203
20    141
11    113
28     96
21     94
3      83
1      66
9      63
2      63
14     62
18     58
24     44
22     26
23     24
17     22
15     21
29     19
8      13
27      8
7       6
25      6
30      3
6       3
26      2
Name: part_of_body, dtype: int64


data['Part of Body'].value_counts()

Head                    918
Fingers                 524
Whole Body              493
Internal Injuries       354
Heart                   318
Ribs                    272
Left Leg                249
Hand                    203
Neck                    141
Feet                    113
Lungs (Puncture)         96
Shoulder                 94
Back                     83
Abdomen                  66
Arm                      63
Face                     63
Hip                      62
Lower Leg                58
Wrist                    44
Multiple                 26
Femur                    24
Forearm                  22
Knee                     21
Lungs (Asphyxiation)     19
Right Eye                13
0                        12
Liver                     8
Elbow                     6
Kidney                    2
Name: Part of Body, dtype: int64


data[data['Part of Body']=='0']


data.drop([153,1149,1554,1562,1799,2130,2378,2858,3044,4246,4420,4797],axis=0,inplace=True)


data[data['Part of Body']=='0']


data['part_of_body'].nunique()

28


data['Part of Body'].nunique()

28


data['event_type'].value_counts()

5     1073
1     1030
2     1027
14     564
13     178
12     154
6      119
4      117
8       68
3        8
7        7
9        4
10       3
11       3
Name: event_type, dtype: int64


data['Event type'].value_counts()

Fall (from elevation)        1073
Struck-by                    1030
Caught in or between         1027
Other                         564
Shock                         178
Card-vascular/resp. fail.     154
Struck against                119
Fall (same level)             117
Inhalation                     68
Bite/sting/scratch              8
Rubbed/abraded                  7
Ingestion                       4
Absorption                      3
Rep. Motion/pressure            3
Name: Event type, dtype: int64


data['evn_factor'].value_counts()

18    1800
8      586
13     545
1      386
6      279
2      174
3      105
5      104
17      95
7       67
11      60
15      46
9       36
14      30
10      29
16      11
12       2
Name: evn_factor, dtype: int64


data['Environmental Factor'].value_counts()

Other                                     1800
Materials Handling Equip./Method           586
Work-Surface/Facility-Layout Condition     545
Pinch Point Action                         386
Overhead Moving/Falling Object Action      279
Catch Point/Puncture Action                174
Shear Point Action                         105
Flying Object Action                       104
Weather, Earthquake, Etc.                   95
Gas/Vapor/Mist/Fume/Smoke/Dust              67
Temperature +/- Tolerance Lev.              60
Overpressure/Underpressure                  46
Chemical Action/Reaction Expos              36
Illumination                                30
Flammable Liquid/Solid Exposure             29
Sound Level                                 11
Radiation Condition                          2
Name: Environmental Factor, dtype: int64


data['hum_factor'].value_counts()

14    1635
1     1273
9      244
10     193
11     141
17     138
20     134
6      130
18     126
13     104
4       96
5       38
8       36
12      30
7       14
15      13
19       9
16       1
Name: hum_factor, dtype: int64


data['Human Factor'].value_counts()

Other                                                    1635
Misjudgment, Hazardous Situation                         1273
Safety Devices Removed/Inoperable                         244
Position Inappropriate For Task                           193
Mater-Handling Procedure Inappropriate                    141
Insufficient /Lack/Engineering Controls                   138
Insufficient /Lack/Protective Work Clothing/Equipment     134
Equipment Inappropriate For Operation                     130
Insufficient /Lack/Written Work Practice Program          126
Lockout/Tagout Procedure Malfunction                      104
Malfunction In Securing/Warning Op                         96
Distracting Actions By Others                              38
Perception Malfunction Task-Environment                    36
Defective Equipment In Use                                 30
Malfunction Neuromuscular System                           14
Insufficient/Lack/Housekeeping Program                     13
Insufficient /Lack/Respiratory Protection                   9
Insufficient /Lack/Expose/Biological Monitoring.            1
Name: Human Factor, dtype: int64


data['task_assigned'].value_counts()

1    2827
2    1528
Name: task_assigned, dtype: int64


data['Task Assigned'].value_counts()

Regularly Assigned        2827
Not Regularly Assigned    1528
Name: Task Assigned, dtype: int64


data['hazsub'].value_counts()

0       4306
8870      13
8880       3
8330       3
240        2
1900       2
614        2
1640       2
170        2
640        2
1340       1
S777       1
1803       1
337        1
L130       1
X100       1
D150       1
1460       1
5010       1
1430       1
C730       1
1480       1
8400       1
2170       1
8890       1
560        1
2150       1
2037       1
Name: hazsub, dtype: int64


data['fat_cause'].value_counts()

0     3551
15     129
26      88
16      77
14      69
18      64
20      43
30      38
25      31
9       27
7       26
4       21
27      20
3       18
5       18
10      17
19      15
21      14
28      12
12      12
29      10
22      10
1        9
17       8
6        7
2        7
23       5
8        4
24       3
11       2
Name: fat_cause, dtype: int64


data['fall_ht'].value_counts()

0    4355
Name: fall_ht, dtype: int64


data.head(2)


data.drop(['summary_nr','con_end','build_stor' , 'Event Description' ,'Event Keywords', 'Abstract Text' , 'proj_cost'
           , 'proj_type' , 'nature_of_inj' , 'part_of_body' , 'event_type' , 'evn_factor' 
           , 'hum_factor' , 'task_assigned' , 'fall_ht' , 'hazsub' , 'fat_cause'],
          axis=1,inplace=True)


data.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 4355 entries, 0 to 4846
Data columns (total 19 columns):
 #   Column                Non-Null Count  Dtype         
---  ------                --------------  -----         
 0   Event Date            4355 non-null   datetime64[ns]
 1   Construction End Use  4355 non-null   object        
 2   Building Stories      4355 non-null   object        
 3   Project Cost          4355 non-null   object        
 4   Project Type          4355 non-null   object        
 5   Degree of Injury      4355 non-null   object        
 6   Nature of Injury      4355 non-null   object        
 7   Part of Body          4355 non-null   object        
 8   Event type            4355 non-null   object        
 9   Environmental Factor  4355 non-null   object        
 10  Human Factor          4355 non-null   object        
 11  Task Assigned         4355 non-null   object        
 12  Event DayOfWeek       4355 non-null   object        
 13  Event Day             4355 non-null   int64         
 14  Event Month           4355 non-null   int64         
 15  Event Year            4355 non-null   int64         
 16  Time                  4355 non-null   object        
 17  Hour                  4355 non-null   int64         
 18  Minutes               4355 non-null   int64         
dtypes: datetime64[ns](1), int64(5), object(13)
memory usage: 680.5+ KB


data


sns.pairplot(data,hue='Degree of Injury',palette='Set1')

<seaborn.axisgrid.PairGrid at 0x214a2c7c1f0>


plt.figure(figsize=(8,5))
sns.heatmap(data.corr(),annot=True,cmap='coolwarm')

<AxesSubplot:>


plt.figure(figsize=(12,8))
chart_count = sns.countplot(x='Part of Body',data=data,hue='Degree of Injury',palette='Set2')
chart_count.set_xticklabels(chart_count.get_xticklabels(),
                            rotation=45,horizontalalignment='right',fontweight='light', fontsize='x-large')
plt.legend()

<matplotlib.legend.Legend at 0x214a62474c0>


plt.figure(figsize=(12,8))
chart_count = sns.countplot(x='Part of Body',data=data,palette='Set2')
chart_count.set_xticklabels(chart_count.get_xticklabels(), rotation=45,horizontalalignment='right',fontweight='light', fontsize='x-large')

[Text(0, 0, 'Fingers'),
 Text(1, 0, 'Hand'),
 Text(2, 0, 'Head'),
 Text(3, 0, 'Neck'),
 Text(4, 0, 'Whole Body'),
 Text(5, 0, 'Internal Injuries'),
 Text(6, 0, 'Ribs'),
 Text(7, 0, 'Lower Leg'),
 Text(8, 0, 'Heart'),
 Text(9, 0, 'Femur'),
 Text(10, 0, 'Abdomen'),
 Text(11, 0, 'Feet'),
 Text(12, 0, 'Face'),
 Text(13, 0, 'Left Leg'),
 Text(14, 0, 'Lungs (Puncture)'),
 Text(15, 0, 'Lungs (Asphyxiation)'),
 Text(16, 0, 'Back'),
 Text(17, 0, 'Shoulder'),
 Text(18, 0, 'Arm'),
 Text(19, 0, 'Wrist'),
 Text(20, 0, 'Hip'),
 Text(21, 0, 'Right Eye'),
 Text(22, 0, 'Multiple'),
 Text(23, 0, 'Forearm'),
 Text(24, 0, 'Knee'),
 Text(25, 0, 'Liver'),
 Text(26, 0, 'Kidney'),
 Text(27, 0, 'Elbow')]


plt.figure(figsize=(12,8))
chart_count = sns.countplot(x='Construction End Use',data=data[data['Construction End Use']!=' '],
                            hue='Degree of Injury',palette='viridis')
chart_count.set_xticklabels(chart_count.get_xticklabels(), rotation=45,horizontalalignment='right',fontweight='light',
                            fontsize='x-large')

[Text(0, 0, 'Other building'),
 Text(1, 0, 'Commercial building'),
 Text(2, 0, 'Sewer/water treatment plant'),
 Text(3, 0, 'Multi-family dwelling'),
 Text(4, 0, 'Highway, road, street'),
 Text(5, 0, 'Pipeline'),
 Text(6, 0, 'Single family or duplex dwelling'),
 Text(7, 0, 'Manufacturing plant'),
 Text(8, 0, 'Power line, transmission line'),
 Text(9, 0, "Contractor's yard/facility"),
 Text(10, 0, 'Power plant'),
 Text(11, 0, 'Other heavy construction'),
 Text(12, 0, 'Bridge'),
 Text(13, 0, 'Tower, tank, storage ,elevator'),
 Text(14, 0, 'Shoreline development, dam, reservoir'),
 Text(15, 0, 'Excavation, landfill'),
 Text(16, 0, 'Refinery')]


plt.figure(figsize=(12,8))
chart_count = sns.countplot(x='Building Stories',data=data[data['Building Stories']!=' '],hue='Degree of Injury',palette='CMRmap')
chart_count.set_xticklabels(chart_count.get_xticklabels(), rotation=45,horizontalalignment='right',fontweight='light', fontsize='x-large')
plt.legend(bbox_to_anchor=(1,1))

<matplotlib.legend.Legend at 0x214a5065af0>


plt.figure(figsize=(12,8))
chart_count = sns.countplot(x='Project Cost',data=data[data['Project Cost']!=' '],hue='Degree of Injury',palette='cool')
chart_count.set_xticklabels(chart_count.get_xticklabels(), rotation=45,horizontalalignment='right',fontweight='light', fontsize='x-large')

[Text(0, 0, '$500,000 to $1,000,000'),
 Text(1, 0, '$5,000,000 to $10,000,000'),
 Text(2, 0, 'Under $50,000'),
 Text(3, 0, '$1,000,000 to $5,000,000'),
 Text(4, 0, '$50,000 to $250,000'),
 Text(5, 0, '$250,000 to $500,000'),
 Text(6, 0, '$10,000,000 to $20,000,000')]


plt.figure(figsize=(12,8))
chart_count = sns.countplot(x='Project Type',data=data[data['Project Type']!=' '],hue='Degree of Injury',palette='coolwarm')
chart_count.set_xticklabels(chart_count.get_xticklabels(), rotation=45,horizontalalignment='right',fontweight='light', fontsize='x-large')

[Text(0, 0, 'Alteration or rehabilitation'),
 Text(1, 0, 'New project or new addition'),
 Text(2, 0, 'Demolition'),
 Text(3, 0, 'Maintenance or repair'),
 Text(4, 0, 'Other')]


plt.figure(figsize=(12,8))
chart_count = sns.countplot(x='Degree of Injury',data=data,palette='Spectral')
chart_count.set_xticklabels(chart_count.get_xticklabels(), rotation=45,horizontalalignment='right',fontweight='light', fontsize='x-large')

[Text(0, 0, 'Nonfatal'), Text(1, 0, 'Fatal')]


plt.figure(figsize=(12,8))
chart_count = sns.countplot(x='Nature of Injury',data=data,hue='Degree of Injury',palette='afmhot')
chart_count.set_xticklabels(chart_count.get_xticklabels(), rotation=45,horizontalalignment='right',fontweight='light', fontsize='x-large')

[Text(0, 0, 'Amputation, Crushing'),
 Text(1, 0, 'Dislocation'),
 Text(2, 0, 'Fire Burn'),
 Text(3, 0, 'Serious Fall/Strike'),
 Text(4, 0, 'Bruising, Contusion'),
 Text(5, 0, 'Fracture, Broken Bones'),
 Text(6, 0, 'Head Trauma'),
 Text(7, 0, 'Heat Exhaustion'),
 Text(8, 0, 'Laceration'),
 Text(9, 0, 'Electrocution'),
 Text(10, 0, 'Asphyxiation, Drowning'),
 Text(11, 0, 'Chemical Burn'),
 Text(12, 0, 'Puncture'),
 Text(13, 0, 'Fall/strike'),
 Text(14, 0, 'Eye injury'),
 Text(15, 0, 'Freezer burn'),
 Text(16, 0, 'Poison'),
 Text(17, 0, 'Fall from Elevation'),
 Text(18, 0, 'Illness')]


plt.figure(figsize=(12,8))
chart_count = sns.countplot(x='Nature of Injury',data=data)
chart_count.set_xticklabels(chart_count.get_xticklabels(), rotation=45,horizontalalignment='right', fontsize='x-large')

[Text(0, 0, 'Amputation, Crushing'),
 Text(1, 0, 'Dislocation'),
 Text(2, 0, 'Fire Burn'),
 Text(3, 0, 'Serious Fall/Strike'),
 Text(4, 0, 'Bruising, Contusion'),
 Text(5, 0, 'Fracture, Broken Bones'),
 Text(6, 0, 'Head Trauma'),
 Text(7, 0, 'Heat Exhaustion'),
 Text(8, 0, 'Laceration'),
 Text(9, 0, 'Electrocution'),
 Text(10, 0, 'Asphyxiation, Drowning'),
 Text(11, 0, 'Chemical Burn'),
 Text(12, 0, 'Puncture'),
 Text(13, 0, 'Fall/strike'),
 Text(14, 0, 'Eye injury'),
 Text(15, 0, 'Freezer burn'),
 Text(16, 0, 'Poison'),
 Text(17, 0, 'Fall from Elevation'),
 Text(18, 0, 'Illness')]


plt.figure(figsize=(12,8))
chart_count = sns.countplot(x='Event type',data=data[data['Event type']!='Other'],hue='Degree of Injury',palette='bwr')
chart_count.set_xticklabels(chart_count.get_xticklabels(), rotation=45,horizontalalignment='right',fontweight='light', fontsize='x-large')

[Text(0, 0, 'Caught in or between'),
 Text(1, 0, 'Fall (from elevation)'),
 Text(2, 0, 'Struck-by'),
 Text(3, 0, 'Card-vascular/resp. fail.'),
 Text(4, 0, 'Shock'),
 Text(5, 0, 'Struck against'),
 Text(6, 0, 'Inhalation'),
 Text(7, 0, 'Fall (same level)'),
 Text(8, 0, 'Absorption'),
 Text(9, 0, 'Rubbed/abraded'),
 Text(10, 0, 'Bite/sting/scratch'),
 Text(11, 0, 'Rep. Motion/pressure'),
 Text(12, 0, 'Ingestion')]


plt.figure(figsize=(12,8))
chart_count = sns.countplot(x='Environmental Factor',data=data[data['Environmental Factor']!='Other'],hue='Degree of Injury',palette='hot')
chart_count.set_xticklabels(chart_count.get_xticklabels(), rotation=45,horizontalalignment='right',fontweight='light', fontsize='x-large')

[Text(0, 0, 'Catch Point/Puncture Action'),
 Text(1, 0, 'Work-Surface/Facility-Layout Condition'),
 Text(2, 0, 'Overhead Moving/Falling Object Action'),
 Text(3, 0, 'Temperature +/- Tolerance Lev.'),
 Text(4, 0, 'Sound Level'),
 Text(5, 0, 'Flying Object Action'),
 Text(6, 0, 'Materials Handling Equip./Method'),
 Text(7, 0, 'Pinch Point Action'),
 Text(8, 0, 'Chemical Action/Reaction Expos'),
 Text(9, 0, 'Weather, Earthquake, Etc.'),
 Text(10, 0, 'Shear Point Action'),
 Text(11, 0, 'Gas/Vapor/Mist/Fume/Smoke/Dust'),
 Text(12, 0, 'Flammable Liquid/Solid Exposure'),
 Text(13, 0, 'Illumination'),
 Text(14, 0, 'Overpressure/Underpressure'),
 Text(15, 0, 'Radiation Condition')]


plt.figure(figsize=(12,8))
chart_count = sns.countplot(x='Environmental Factor',data=data[data['Environmental Factor']!='Other'],palette='hot')
chart_count.set_xticklabels(chart_count.get_xticklabels(), rotation=45,horizontalalignment='right', fontsize='large')

[Text(0, 0, 'Catch Point/Puncture Action'),
 Text(1, 0, 'Work-Surface/Facility-Layout Condition'),
 Text(2, 0, 'Overhead Moving/Falling Object Action'),
 Text(3, 0, 'Temperature +/- Tolerance Lev.'),
 Text(4, 0, 'Sound Level'),
 Text(5, 0, 'Flying Object Action'),
 Text(6, 0, 'Materials Handling Equip./Method'),
 Text(7, 0, 'Pinch Point Action'),
 Text(8, 0, 'Chemical Action/Reaction Expos'),
 Text(9, 0, 'Weather, Earthquake, Etc.'),
 Text(10, 0, 'Shear Point Action'),
 Text(11, 0, 'Gas/Vapor/Mist/Fume/Smoke/Dust'),
 Text(12, 0, 'Flammable Liquid/Solid Exposure'),
 Text(13, 0, 'Illumination'),
 Text(14, 0, 'Overpressure/Underpressure'),
 Text(15, 0, 'Radiation Condition')]


plt.figure(figsize=(12,8))
chart_count = sns.countplot(x='Human Factor',data=data[data['Human Factor']!='Other'],hue='Degree of Injury',palette='Set1')
chart_count.set_xticklabels(chart_count.get_xticklabels(), rotation=45,horizontalalignment='right',fontweight='light', fontsize='x-large')

[Text(0, 0, 'Position Inappropriate For Task'),
 Text(1, 0, 'Malfunction In Securing/Warning Op'),
 Text(2, 0, 'Insufficient/Lack/Housekeeping Program'),
 Text(3, 0, 'Safety Devices Removed/Inoperable'),
 Text(4, 0, 'Misjudgment, Hazardous Situation'),
 Text(5, 0, 'Equipment Inappropriate For Operation'),
 Text(6, 0, 'Mater-Handling Procedure Inappropriate'),
 Text(7, 0, 'Distracting Actions By Others'),
 Text(8, 0, 'Insufficient /Lack/Protective Work Clothing/Equipment'),
 Text(9, 0, 'Insufficient /Lack/Engineering Controls'),
 Text(10, 0, 'Perception Malfunction Task-Environment'),
 Text(11, 0, 'Insufficient /Lack/Written Work Practice Program'),
 Text(12, 0, 'Lockout/Tagout Procedure Malfunction'),
 Text(13, 0, 'Defective Equipment In Use'),
 Text(14, 0, 'Malfunction Neuromuscular System'),
 Text(15, 0, 'Insufficient /Lack/Respiratory Protection'),
 Text(16, 0, 'Insufficient /Lack/Expose/Biological Monitoring.')]


plt.figure(figsize=(12,8))
chart_count = sns.countplot(x='Human Factor',data=data[data['Human Factor']!='Other'],palette='Set1')
chart_count.set_xticklabels(chart_count.get_xticklabels(), rotation=45,horizontalalignment='right', fontsize='medium')

[Text(0, 0, 'Position Inappropriate For Task'),
 Text(1, 0, 'Malfunction In Securing/Warning Op'),
 Text(2, 0, 'Insufficient/Lack/Housekeeping Program'),
 Text(3, 0, 'Safety Devices Removed/Inoperable'),
 Text(4, 0, 'Misjudgment, Hazardous Situation'),
 Text(5, 0, 'Equipment Inappropriate For Operation'),
 Text(6, 0, 'Mater-Handling Procedure Inappropriate'),
 Text(7, 0, 'Distracting Actions By Others'),
 Text(8, 0, 'Insufficient /Lack/Protective Work Clothing/Equipment'),
 Text(9, 0, 'Insufficient /Lack/Engineering Controls'),
 Text(10, 0, 'Perception Malfunction Task-Environment'),
 Text(11, 0, 'Insufficient /Lack/Written Work Practice Program'),
 Text(12, 0, 'Lockout/Tagout Procedure Malfunction'),
 Text(13, 0, 'Defective Equipment In Use'),
 Text(14, 0, 'Malfunction Neuromuscular System'),
 Text(15, 0, 'Insufficient /Lack/Respiratory Protection'),
 Text(16, 0, 'Insufficient /Lack/Expose/Biological Monitoring.')]


plt.figure(figsize=(12,8))
sns.countplot(x='Task Assigned',data=data,hue='Degree of Injury',palette='twilight')

<AxesSubplot:xlabel='Task Assigned', ylabel='count'>


plt.figure(figsize=(12,8))
chart_count = sns.countplot(x='Event DayOfWeek',data=data)
chart_count.set_xticklabels(chart_count.get_xticklabels(), rotation=45,horizontalalignment='right',fontweight='light', fontsize='x-large')

[Text(0, 0, 'Thu'),
 Text(1, 0, 'Mon'),
 Text(2, 0, 'Fri'),
 Text(3, 0, 'Wed'),
 Text(4, 0, 'Tue'),
 Text(5, 0, 'Sun'),
 Text(6, 0, 'Sat')]


plt.figure(figsize=(12,8))
chart_count = sns.countplot(x='Event DayOfWeek',data=data,hue='Degree of Injury',palette='twilight')
chart_count.set_xticklabels(chart_count.get_xticklabels(), rotation=45,horizontalalignment='right',fontweight='light', fontsize='x-large')

[Text(0, 0, 'Thu'),
 Text(1, 0, 'Mon'),
 Text(2, 0, 'Fri'),
 Text(3, 0, 'Wed'),
 Text(4, 0, 'Tue'),
 Text(5, 0, 'Sun'),
 Text(6, 0, 'Sat')]


plt.figure(figsize=(12,8))
sns.histplot(x='Hour',data=data,bins=10)

<AxesSubplot:xlabel='Hour', ylabel='Count'>


sns.kdeplot(data=data,x='Hour')

<AxesSubplot:xlabel='Hour', ylabel='Density'>


plt.figure(figsize=(12,8))
chart_count = sns.countplot(x='Event Day',data=data,hue='Degree of Injury',palette='twilight')
chart_count.set_xticklabels(chart_count.get_xticklabels(), rotation=45,horizontalalignment='right',fontweight='light', fontsize='x-large')

[Text(0, 0, '1'),
 Text(1, 0, '2'),
 Text(2, 0, '3'),
 Text(3, 0, '4'),
 Text(4, 0, '5'),
 Text(5, 0, '6'),
 Text(6, 0, '7'),
 Text(7, 0, '8'),
 Text(8, 0, '9'),
 Text(9, 0, '10'),
 Text(10, 0, '11'),
 Text(11, 0, '12'),
 Text(12, 0, '13'),
 Text(13, 0, '14'),
 Text(14, 0, '15'),
 Text(15, 0, '16'),
 Text(16, 0, '17'),
 Text(17, 0, '18'),
 Text(18, 0, '19'),
 Text(19, 0, '20'),
 Text(20, 0, '21'),
 Text(21, 0, '22'),
 Text(22, 0, '23'),
 Text(23, 0, '24'),
 Text(24, 0, '25'),
 Text(25, 0, '26'),
 Text(26, 0, '27'),
 Text(27, 0, '28'),
 Text(28, 0, '29'),
 Text(29, 0, '30'),
 Text(30, 0, '31')]


plt.figure(figsize=(12,8))
chart_count = sns.countplot(x='Event Month',data=data,hue='Degree of Injury',palette='twilight')
chart_count.set_xticklabels(chart_count.get_xticklabels(), rotation=45,horizontalalignment='right',fontweight='light', fontsize='x-large')

[Text(0, 0, '1'),
 Text(1, 0, '2'),
 Text(2, 0, '3'),
 Text(3, 0, '4'),
 Text(4, 0, '5'),
 Text(5, 0, '6'),
 Text(6, 0, '7'),
 Text(7, 0, '8'),
 Text(8, 0, '9'),
 Text(9, 0, '10'),
 Text(10, 0, '11'),
 Text(11, 0, '12')]


plt.figure(figsize=(12,8))
chart_count = sns.countplot(x='Event Year',data=data,hue='Degree of Injury',palette='twilight')
chart_count.set_xticklabels(chart_count.get_xticklabels(), rotation=45,horizontalalignment='right',fontweight='light', fontsize='x-large')

[Text(0, 0, '2015'), Text(1, 0, '2016'), Text(2, 0, '2017')]


data.columns

Index(['Event Date', 'Construction End Use', 'Building Stories',
       'Project Cost', 'Project Type', 'Degree of Injury', 'Nature of Injury',
       'Part of Body', 'Event type', 'Environmental Factor', 'Human Factor',
       'Task Assigned', 'Event DayOfWeek', 'Event Day', 'Event Month',
       'Event Year', 'Time', 'Hour', 'Minutes'],
      dtype='object')


sns.set_style('whitegrid')
plt.figure(figsize=(10,6))
sns.jointplot(data=data[(data['Building Stories']!=' ')&(data['Construction End Use']!=' ')],
              x='Building Stories',y='Construction End Use',color='green')

<seaborn.axisgrid.JointGrid at 0x214aaf4ffd0>

<Figure size 720x432 with 0 Axes>


plt.figure(figsize=(10,6))
sns.jointplot(data=data,
              x='Hour',y='Event DayOfWeek',color='blue')

<seaborn.axisgrid.JointGrid at 0x214ab0edc10>

<Figure size 720x432 with 0 Axes>


temp = data[data['Part of Body'].isin(['Head','Fingers','Whole Body','Internal Injuries',
                                       'Heart','Ribs','Left Leg','Hand','Neck','Feet'])]


sns.jointplot(data=temp,
              x='Part of Body',y='Event type',color='red',height=10)

<seaborn.axisgrid.JointGrid at 0x214a61e7b80>


data['Human Factor'].value_counts()

Other                                                    1635
Misjudgment, Hazardous Situation                         1273
Safety Devices Removed/Inoperable                         244
Position Inappropriate For Task                           193
Mater-Handling Procedure Inappropriate                    141
Insufficient /Lack/Engineering Controls                   138
Insufficient /Lack/Protective Work Clothing/Equipment     134
Equipment Inappropriate For Operation                     130
Insufficient /Lack/Written Work Practice Program          126
Lockout/Tagout Procedure Malfunction                      104
Malfunction In Securing/Warning Op                         96
Distracting Actions By Others                              38
Perception Malfunction Task-Environment                    36
Defective Equipment In Use                                 30
Malfunction Neuromuscular System                           14
Insufficient/Lack/Housekeeping Program                     13
Insufficient /Lack/Respiratory Protection                   9
Insufficient /Lack/Expose/Biological Monitoring.            1
Name: Human Factor, dtype: int64


temp1 = data[data['Human Factor'].isin(['Misjudgment, Hazardous Situation','Safety Devices Removed/Inoperable',
    'Position Inappropriate For Task','Mater-Handling Procedure Inappropriate'
])]


sns.set_style('whitegrid')
fig = plt.figure(figsize=(12,8))
sns.jointplot(data=temp1,
              x='Human Factor',y='Event type',height=11)
locs,labels = plt.xticks()

<Figure size 864x576 with 0 Axes>


datasweet= pd.get_dummies(data,columns=['Degree of Injury'])


datasweet


#analyzing the dataset
advert_report = sv.analyze([datasweet,'Construction Accident'],target_feat='Degree of Injury_Nonfatal',pairwise_analysis="on")
#display the report
advert_report.show_notebook(w='100%')


datasweet.drop('Event Date',inplace=True,axis=1)


my_report = sv.compare_intra(datasweet, datasweet['Degree of Injury_Nonfatal'] ==0, ["Fatal", "NonFatal"])
my_report.show_notebook(w='100%')


df = data.copy()
df.drop(['Event Date','Event Day','Event Month','Event Year','Hour','Minutes'],axis=1,inplace=True)


df = pd.get_dummies(df)


from mlxtend.frequent_patterns import apriori, association_rules


apriori = apriori(df, min_support = 0.5, use_colnames = True, verbose = 1)
apriori.sort_values(by='support',ascending=False).head(30)

Processing 20 combinations | Sampling itemset size 4

C:\ProgramData\Anaconda3\lib\site-packages\mlxtend\frequent_patterns\fpcommon.py:110: DeprecationWarning: DataFrames with non-bool types result in worse computationalperformance and their support might be discontinued in the future.Please use a DataFrame with bool type
  warnings.warn(


rules = association_rules(apriori, metric = "support", min_threshold = 0.1)
rules.head(30)


rules[rules['lift'] >= 1].sort_values(by='lift',ascending=False).head(30)


plt.scatter(rules['support'], rules['confidence'], alpha=0.5)
plt.xlabel('support')
plt.ylabel('confidence')
plt.title('Support vs Confidence')
plt.show()


plt.scatter(rules['support'], rules['lift'], alpha=0.5)
plt.xlabel('support')
plt.ylabel('lift')
plt.title('Support vs Lift')
plt.show()


fit = np.polyfit(rules['lift'], rules['confidence'], 1)
fit_fn = np.poly1d(fit)
plt.plot(rules['lift'], rules['confidence'], 'yo', rules['lift'], 
 fit_fn(rules['lift']))

[<matplotlib.lines.Line2D at 0x214b52c14c0>,
 <matplotlib.lines.Line2D at 0x214b52c1490>]


data.drop(['Construction End Use','Building Stories','Project Cost','Project Type','Time','Event Date'],axis=1,inplace=True)


data.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 4355 entries, 0 to 4846
Data columns (total 13 columns):
 #   Column                Non-Null Count  Dtype 
---  ------                --------------  ----- 
 0   Degree of Injury      4355 non-null   object
 1   Nature of Injury      4355 non-null   object
 2   Part of Body          4355 non-null   object
 3   Event type            4355 non-null   object
 4   Environmental Factor  4355 non-null   object
 5   Human Factor          4355 non-null   object
 6   Task Assigned         4355 non-null   object
 7   Event DayOfWeek       4355 non-null   object
 8   Event Day             4355 non-null   int64 
 9   Event Month           4355 non-null   int64 
 10  Event Year            4355 non-null   int64 
 11  Hour                  4355 non-null   int64 
 12  Minutes               4355 non-null   int64 
dtypes: int64(5), object(8)
memory usage: 605.4+ KB


data.head()


enc = HashingEncoder(verbose=1,n_components=3,max_process=1)


data_NOI = enc.fit_transform(data['Nature of Injury'],axis=1)


data_NOI


data_NOI.rename(columns={'col_0':'NOI_1','col_1':'NOI_2','col_2':'NOI_3'},inplace=True)


data_NOI


enc = HashingEncoder(verbose=1,n_components=4,max_process=1)


data_POB = enc.fit_transform(data['Part of Body'],axis=1)


data_POB


data_POB.rename(columns={'col_0':'POB_1','col_1':'POB_2','col_2':'POB_3','col_3':'POB_4'},inplace=True)


data_POB


enc = HashingEncoder(verbose=1,n_components=3,max_process=1)


data_ET = enc.fit_transform(data['Event type'],axis=1)


data_ET.rename(columns={'col_0':'ET_1','col_1':'ET_2','col_2':'ET_3'},inplace=True)


data_ET


data_EF = enc.fit_transform(data['Environmental Factor'],axis=1)


data_EF.rename(columns={'col_0':'EF_1','col_1':'EF_2','col_2':'EF_3'},inplace=True)


data_EF


data_HF = enc.fit_transform(data['Human Factor'],axis=1)


data_HF.rename(columns={'col_0':'HF_1','col_1':'HF_2','col_2':'HF_3'},inplace=True)


data_HF


data_EDOW = enc.fit_transform(data['Event DayOfWeek'],axis=1)


data_EDOW.rename(columns={'col_0':'EDOW_1','col_1':'EDOW_2','col_2':'EDOW_3'},inplace=True)


data_EDOW


data = pd.concat((data_NOI,data_POB,data_ET,data_EF,data_HF,data_EDOW,data['Task Assigned'],data['Event Day'],
                  data['Event Month'],data['Event Year'],data['Hour'],data['Minutes'],
                  data['Degree of Injury']),axis=1)


data


data = pd.get_dummies(data,columns=['Degree of Injury','Task Assigned'],drop_first=True)
data


from fast_ml.model_development import train_valid_test_split


X_train, y_train, X_valid, y_valid, X_test, y_test = train_valid_test_split(data, target = 'Degree of Injury_Nonfatal', 
                                                                            train_size=0.7, valid_size=0.1, test_size=0.2,
                                                                           random_state=101)

print(X_train.shape), print(y_train.shape)
print(X_valid.shape), print(y_valid.shape)
print(X_test.shape), print(y_test.shape)

(3048, 25)
(3048,)
(435, 25)
(435,)
(872, 25)
(872,)

(None, None)


X_train


y_train

3567    0
3483    0
3588    0
335     1
2910    0
       ..
4549    0
4649    0
665     0
1472    0
1681    1
Name: Degree of Injury_Nonfatal, Length: 3048, dtype: uint8


X_valid


y_valid

3857    0
3011    0
1838    1
1824    1
2283    1
       ..
2241    1
2258    1
2047    1
72      0
2116    0
Name: Degree of Injury_Nonfatal, Length: 435, dtype: uint8


X_train


y_test

2570    0
3919    0
1013    1
370     0
1941    0
       ..
1498    1
1020    1
2285    1
577     1
260     0
Name: Degree of Injury_Nonfatal, Length: 872, dtype: uint8


from sklearn.linear_model import LogisticRegression


lr = LogisticRegression(random_state=101)


lr.fit(X_train,y_train)

LogisticRegression(random_state=101)

LogisticRegression(random_state=101)


pred = lr.predict(X_valid)


print('Score:\n',lr.score(X_valid,y_valid),'\n')

from sklearn.metrics import confusion_matrix
print('Confusion Matrix:\n',confusion_matrix(y_valid,pred),'\n')

from sklearn.metrics import classification_report
print('Classification Report:\n',classification_report(y_valid,pred))

Score:
 0.7931034482758621 

Confusion Matrix:
 [[210  48]
 [ 42 135]] 

Classification Report:
               precision    recall  f1-score   support

           0       0.83      0.81      0.82       258
           1       0.74      0.76      0.75       177

    accuracy                           0.79       435
   macro avg       0.79      0.79      0.79       435
weighted avg       0.79      0.79      0.79       435


mat_T = confusion_matrix(y_valid,pred)
sns.heatmap(mat_T, square=True, annot=True,fmt='d',cbar=False)
plt.xlabel('true label')
plt.ylabel('predicted label');


df_eval = pd.DataFrame({'Label':y_valid,
                        'Prediction': pred})
df_eval


from sklearn.preprocessing import StandardScaler


scaler = StandardScaler()


scaler.fit(data.drop(['Degree of Injury_Nonfatal'],axis=1))

StandardScaler()

StandardScaler()


scaled_features = scaler.transform(data.drop(['Degree of Injury_Nonfatal'],axis=1))


df_feat = pd.DataFrame(scaled_features,columns = data.columns[:-1])
df_feat


from sklearn.neighbors import KNeighborsClassifier
knn = KNeighborsClassifier(n_neighbors=1)
knn.fit(X_train,y_train)
pred = knn.predict(X_valid)
print('Score:\n',knn.score(X_valid,y_valid),'\n')

from sklearn.metrics import confusion_matrix
print('Confusion Matrix:\n',confusion_matrix(y_valid,pred),'\n')

from sklearn.metrics import classification_report
print('Classification Report:\n',classification_report(y_valid,pred))

Score:
 0.7632183908045977 

Confusion Matrix:
 [[198  60]
 [ 43 134]] 

Classification Report:
               precision    recall  f1-score   support

           0       0.82      0.77      0.79       258
           1       0.69      0.76      0.72       177

    accuracy                           0.76       435
   macro avg       0.76      0.76      0.76       435
weighted avg       0.77      0.76      0.76       435


mat_T = confusion_matrix(y_valid,pred)
sns.heatmap(mat_T, square=True, annot=True,fmt='d',cbar=False)
plt.xlabel('true label')
plt.ylabel('predicted label');


error_rate = []
for i in range(1,40):
    knn = KNeighborsClassifier(n_neighbors= i)
    knn.fit(X_train,y_train)
    pred_i = knn.predict(X_valid)
    error_rate.append(np.mean(pred_i != y_valid))


plt.figure(figsize=(10,6))
plt.plot(range(1,40),error_rate,color='blue',ls='--',marker='o',markerfacecolor='red',markersize=10)
plt.title('ERROR RATE VS. K VALUES')
plt.xlabel('K')
plt.ylabel('ERROR RATE')

Text(0, 0.5, 'ERROR RATE')


from sklearn.neighbors import KNeighborsClassifier
knn = KNeighborsClassifier(n_neighbors=5)
knn.fit(X_train,y_train)
pred = knn.predict(X_valid)
print('Score:\n',knn.score(X_valid,y_valid),'\n')

from sklearn.metrics import confusion_matrix
print('Confusion Matrix:\n',confusion_matrix(y_valid,pred),'\n')

from sklearn.metrics import classification_report
print('Classification Report:\n',classification_report(y_valid,pred))

Score:
 0.7655172413793103 

Confusion Matrix:
 [[191  67]
 [ 35 142]] 

Classification Report:
               precision    recall  f1-score   support

           0       0.85      0.74      0.79       258
           1       0.68      0.80      0.74       177

    accuracy                           0.77       435
   macro avg       0.76      0.77      0.76       435
weighted avg       0.78      0.77      0.77       435


mat_T = confusion_matrix(y_valid,pred)
sns.heatmap(mat_T, square=True, annot=True,fmt='d',cbar=False)
plt.xlabel('true label')
plt.ylabel('predicted label');


from sklearn.tree import DecisionTreeClassifier


dt = DecisionTreeClassifier(random_state=101)


dt.fit(X_train,y_train)

DecisionTreeClassifier(random_state=101)

DecisionTreeClassifier(random_state=101)


pred = dt.predict(X_valid)


print('Score:\n',dt.score(X_valid,y_valid),'\n')

from sklearn.metrics import confusion_matrix
print('Confusion Matrix:\n',confusion_matrix(y_valid,pred),'\n')

from sklearn.metrics import classification_report
print('Classification Report:\n',classification_report(y_valid,pred))

Score:
 0.8367816091954023 

Confusion Matrix:
 [[220  38]
 [ 33 144]] 

Classification Report:
               precision    recall  f1-score   support

           0       0.87      0.85      0.86       258
           1       0.79      0.81      0.80       177

    accuracy                           0.84       435
   macro avg       0.83      0.83      0.83       435
weighted avg       0.84      0.84      0.84       435


mat_T = confusion_matrix(y_valid,pred)
sns.heatmap(mat_T, square=True, annot=True,fmt='d',cbar=False)
plt.xlabel('true label')
plt.ylabel('predicted label');


df_eval = pd.DataFrame({'Label':y_valid,
                        'Prediction': pred})
df_eval


from sklearn.tree import plot_tree
plt.figure(figsize=(180,120))
plot_tree(dt, filled=True)
plt.title("Decision Tree")
plt.savefig('Decision Tree Visualize.jpg')


pd.DataFrame(data=dt.feature_importances_,index=X_train.columns,columns=['Value']).sort_values(by='Value',ascending=False)


param_grid = {'criterion':['gini','entropy'],'splitter':['best', 'random']}


from sklearn.model_selection import GridSearchCV
grid1 = GridSearchCV(DecisionTreeClassifier(random_state=101),param_grid,refit=True,verbose=3)
grid1.fit(X_train,y_train)
pred = grid1.predict(X_valid)

Fitting 5 folds for each of 4 candidates, totalling 20 fits
[CV 1/5] END .....criterion=gini, splitter=best;, score=0.813 total time=   0.0s
[CV 2/5] END .....criterion=gini, splitter=best;, score=0.828 total time=   0.0s
[CV 3/5] END .....criterion=gini, splitter=best;, score=0.813 total time=   0.0s
[CV 4/5] END .....criterion=gini, splitter=best;, score=0.834 total time=   0.0s
[CV 5/5] END .....criterion=gini, splitter=best;, score=0.800 total time=   0.0s
[CV 1/5] END ...criterion=gini, splitter=random;, score=0.798 total time=   0.0s
[CV 2/5] END ...criterion=gini, splitter=random;, score=0.833 total time=   0.0s
[CV 3/5] END ...criterion=gini, splitter=random;, score=0.803 total time=   0.0s
[CV 4/5] END ...criterion=gini, splitter=random;, score=0.824 total time=   0.0s
[CV 5/5] END ...criterion=gini, splitter=random;, score=0.824 total time=   0.0s
[CV 1/5] END ..criterion=entropy, splitter=best;, score=0.795 total time=   0.0s
[CV 2/5] END ..criterion=entropy, splitter=best;, score=0.841 total time=   0.0s
[CV 3/5] END ..criterion=entropy, splitter=best;, score=0.795 total time=   0.0s
[CV 4/5] END ..criterion=entropy, splitter=best;, score=0.837 total time=   0.0s
[CV 5/5] END ..criterion=entropy, splitter=best;, score=0.823 total time=   0.0s
[CV 1/5] END criterion=entropy, splitter=random;, score=0.816 total time=   0.0s
[CV 2/5] END criterion=entropy, splitter=random;, score=0.846 total time=   0.0s
[CV 3/5] END criterion=entropy, splitter=random;, score=0.808 total time=   0.0s
[CV 4/5] END criterion=entropy, splitter=random;, score=0.841 total time=   0.0s
[CV 5/5] END criterion=entropy, splitter=random;, score=0.816 total time=   0.0s


grid1.best_params_

{'criterion': 'entropy', 'splitter': 'random'}


print('Score:\n',grid1.score(X_valid,y_valid),'\n')

from sklearn.metrics import confusion_matrix
print('Confusion Matrix:\n',confusion_matrix(y_valid,pred),'\n')

from sklearn.metrics import classification_report
print('Classification Report:\n',classification_report(y_valid,pred))

Score:
 0.8413793103448276 

Confusion Matrix:
 [[219  39]
 [ 30 147]] 

Classification Report:
               precision    recall  f1-score   support

           0       0.88      0.85      0.86       258
           1       0.79      0.83      0.81       177

    accuracy                           0.84       435
   macro avg       0.83      0.84      0.84       435
weighted avg       0.84      0.84      0.84       435


mat_T = confusion_matrix(y_valid,pred)
sns.heatmap(mat_T, square=True, annot=True,fmt='d',cbar=False)
plt.xlabel('true label')
plt.ylabel('predicted label');


from sklearn.ensemble import RandomForestClassifier


rf = RandomForestClassifier(n_estimators=100,random_state=101)


rf.fit(X_train,y_train)

RandomForestClassifier(random_state=101)

RandomForestClassifier(random_state=101)


pred_rf = rf.predict(X_valid)


df_eval = pd.DataFrame({'Label':y_valid,
                        'Prediction': pred_rf
                        })
df_eval


print('Score:\n',rf.score(X_valid,y_valid),'\n')

from sklearn.metrics import confusion_matrix
print('Confusion Matrix:\n',confusion_matrix(y_valid,pred_rf),'\n')

from sklearn.metrics import classification_report
print('Classification Report:\n',classification_report(y_valid,pred_rf))

Score:
 0.8873563218390804 

Confusion Matrix:
 [[229  29]
 [ 20 157]] 

Classification Report:
               precision    recall  f1-score   support

           0       0.92      0.89      0.90       258
           1       0.84      0.89      0.87       177

    accuracy                           0.89       435
   macro avg       0.88      0.89      0.88       435
weighted avg       0.89      0.89      0.89       435


mat_T = confusion_matrix(y_valid,pred_rf)
sns.heatmap(mat_T, square=True, annot=True,fmt='d',cbar=False)
plt.xlabel('true label')
plt.ylabel('predicted label');


pd.DataFrame(data=rf.feature_importances_,index=X_train.columns,columns=['Value']).sort_values(by='Value',ascending=False)


param_grid = {'n_estimators':[50,100,150,200,250,500],'criterion':['gini', 'entropy']}


from sklearn.model_selection import GridSearchCV
grid2 = GridSearchCV(RandomForestClassifier(random_state=101),param_grid,refit=True,verbose=3)
grid2.fit(X_train,y_train)
pred = grid2.predict(X_valid)

Fitting 5 folds for each of 12 candidates, totalling 60 fits
[CV 1/5] END ...criterion=gini, n_estimators=50;, score=0.859 total time=   0.1s
[CV 2/5] END ...criterion=gini, n_estimators=50;, score=0.893 total time=   0.1s
[CV 3/5] END ...criterion=gini, n_estimators=50;, score=0.859 total time=   0.1s
[CV 4/5] END ...criterion=gini, n_estimators=50;, score=0.888 total time=   0.1s
[CV 5/5] END ...criterion=gini, n_estimators=50;, score=0.880 total time=   0.1s
[CV 1/5] END ..criterion=gini, n_estimators=100;, score=0.861 total time=   0.3s
[CV 2/5] END ..criterion=gini, n_estimators=100;, score=0.897 total time=   0.3s
[CV 3/5] END ..criterion=gini, n_estimators=100;, score=0.859 total time=   0.3s
[CV 4/5] END ..criterion=gini, n_estimators=100;, score=0.888 total time=   0.2s
[CV 5/5] END ..criterion=gini, n_estimators=100;, score=0.877 total time=   0.2s
[CV 1/5] END ..criterion=gini, n_estimators=150;, score=0.861 total time=   0.4s
[CV 2/5] END ..criterion=gini, n_estimators=150;, score=0.892 total time=   0.4s
[CV 3/5] END ..criterion=gini, n_estimators=150;, score=0.856 total time=   0.4s
[CV 4/5] END ..criterion=gini, n_estimators=150;, score=0.893 total time=   0.4s
[CV 5/5] END ..criterion=gini, n_estimators=150;, score=0.880 total time=   0.4s
[CV 1/5] END ..criterion=gini, n_estimators=200;, score=0.859 total time=   0.6s
[CV 2/5] END ..criterion=gini, n_estimators=200;, score=0.893 total time=   0.6s
[CV 3/5] END ..criterion=gini, n_estimators=200;, score=0.857 total time=   0.6s
[CV 4/5] END ..criterion=gini, n_estimators=200;, score=0.888 total time=   0.6s
[CV 5/5] END ..criterion=gini, n_estimators=200;, score=0.878 total time=   0.6s
[CV 1/5] END ..criterion=gini, n_estimators=250;, score=0.856 total time=   0.8s
[CV 2/5] END ..criterion=gini, n_estimators=250;, score=0.897 total time=   0.8s
[CV 3/5] END ..criterion=gini, n_estimators=250;, score=0.859 total time=   0.7s
[CV 4/5] END ..criterion=gini, n_estimators=250;, score=0.892 total time=   0.7s
[CV 5/5] END ..criterion=gini, n_estimators=250;, score=0.880 total time=   0.7s
[CV 1/5] END ..criterion=gini, n_estimators=500;, score=0.861 total time=   1.6s
[CV 2/5] END ..criterion=gini, n_estimators=500;, score=0.897 total time=   1.6s
[CV 3/5] END ..criterion=gini, n_estimators=500;, score=0.864 total time=   1.6s
[CV 4/5] END ..criterion=gini, n_estimators=500;, score=0.892 total time=   1.6s
[CV 5/5] END ..criterion=gini, n_estimators=500;, score=0.883 total time=   1.6s
[CV 1/5] END criterion=entropy, n_estimators=50;, score=0.869 total time=   0.1s
[CV 2/5] END criterion=entropy, n_estimators=50;, score=0.907 total time=   0.1s
[CV 3/5] END criterion=entropy, n_estimators=50;, score=0.861 total time=   0.1s
[CV 4/5] END criterion=entropy, n_estimators=50;, score=0.878 total time=   0.1s
[CV 5/5] END criterion=entropy, n_estimators=50;, score=0.885 total time=   0.1s
[CV 1/5] END criterion=entropy, n_estimators=100;, score=0.870 total time=   0.3s
[CV 2/5] END criterion=entropy, n_estimators=100;, score=0.895 total time=   0.3s
[CV 3/5] END criterion=entropy, n_estimators=100;, score=0.859 total time=   0.3s
[CV 4/5] END criterion=entropy, n_estimators=100;, score=0.887 total time=   0.3s
[CV 5/5] END criterion=entropy, n_estimators=100;, score=0.882 total time=   0.3s
[CV 1/5] END criterion=entropy, n_estimators=150;, score=0.869 total time=   0.4s
[CV 2/5] END criterion=entropy, n_estimators=150;, score=0.897 total time=   0.4s
[CV 3/5] END criterion=entropy, n_estimators=150;, score=0.859 total time=   0.5s
[CV 4/5] END criterion=entropy, n_estimators=150;, score=0.892 total time=   0.5s
[CV 5/5] END criterion=entropy, n_estimators=150;, score=0.888 total time=   0.5s
[CV 1/5] END criterion=entropy, n_estimators=200;, score=0.869 total time=   0.6s
[CV 2/5] END criterion=entropy, n_estimators=200;, score=0.893 total time=   0.6s
[CV 3/5] END criterion=entropy, n_estimators=200;, score=0.857 total time=   0.6s
[CV 4/5] END criterion=entropy, n_estimators=200;, score=0.887 total time=   0.6s
[CV 5/5] END criterion=entropy, n_estimators=200;, score=0.885 total time=   0.6s
[CV 1/5] END criterion=entropy, n_estimators=250;, score=0.869 total time=   0.8s
[CV 2/5] END criterion=entropy, n_estimators=250;, score=0.895 total time=   0.8s
[CV 3/5] END criterion=entropy, n_estimators=250;, score=0.859 total time=   0.8s
[CV 4/5] END criterion=entropy, n_estimators=250;, score=0.893 total time=   0.8s
[CV 5/5] END criterion=entropy, n_estimators=250;, score=0.887 total time=   0.9s
[CV 1/5] END criterion=entropy, n_estimators=500;, score=0.862 total time=   1.8s
[CV 2/5] END criterion=entropy, n_estimators=500;, score=0.900 total time=   1.9s
[CV 3/5] END criterion=entropy, n_estimators=500;, score=0.859 total time=   1.7s
[CV 4/5] END criterion=entropy, n_estimators=500;, score=0.892 total time=   1.9s
[CV 5/5] END criterion=entropy, n_estimators=500;, score=0.887 total time=   1.9s


grid2.best_params_

{'criterion': 'entropy', 'n_estimators': 150}


print('Score:\n',grid2.score(X_valid,y_valid),'\n')

from sklearn.metrics import confusion_matrix
print('Confusion Matrix:\n',confusion_matrix(y_valid,pred),'\n')

from sklearn.metrics import classification_report
print('Classification Report:\n',classification_report(y_valid,pred))

Score:
 0.8896551724137931 

Confusion Matrix:
 [[228  30]
 [ 18 159]] 

Classification Report:
               precision    recall  f1-score   support

           0       0.93      0.88      0.90       258
           1       0.84      0.90      0.87       177

    accuracy                           0.89       435
   macro avg       0.88      0.89      0.89       435
weighted avg       0.89      0.89      0.89       435


mat_T = confusion_matrix(y_valid,pred)
sns.heatmap(mat_T, square=True, annot=True,fmt='d',cbar=False)
plt.xlabel('true label')
plt.ylabel('predicted label');


from sklearn.svm import SVC


svm = SVC(random_state=101)


svm.fit(X_train,y_train)

SVC(random_state=101)

SVC(random_state=101)


pred = svm.predict(X_valid)


df_eval = pd.DataFrame({'Label':y_valid,
                        'Prediction': pred
                        })
df_eval


print('Score:\n',svm.score(X_valid,y_valid),'\n')

from sklearn.metrics import confusion_matrix
print('Confusion Matrix:\n',confusion_matrix(y_valid,pred),'\n')

from sklearn.metrics import classification_report
print('Classification Report:\n',classification_report(y_valid,pred))

Score:
 0.593103448275862 

Confusion Matrix:
 [[258   0]
 [177   0]] 

Classification Report:
               precision    recall  f1-score   support

           0       0.59      1.00      0.74       258
           1       0.00      0.00      0.00       177

    accuracy                           0.59       435
   macro avg       0.30      0.50      0.37       435
weighted avg       0.35      0.59      0.44       435


df_eval[df_eval['Label'] != df_eval['Prediction']]


mat_T = confusion_matrix(y_valid,pred)
sns.heatmap(mat_T, square=True, annot=True,fmt='d',cbar=False)
plt.xlabel('true label')
plt.ylabel('predicted label');


param_grid = {'C':[0.001,0.01,0.1,1,10,100,1000],'gamma':[100,10,1,0.1,0.01,0.001,0.0001]}


from sklearn.model_selection import GridSearchCV


grid3 = GridSearchCV(SVC(random_state=101),param_grid,refit=True,verbose=3)


grid3.fit(X_train,y_train)

Fitting 5 folds for each of 49 candidates, totalling 245 fits
[CV 1/5] END ................C=0.001, gamma=100;, score=0.592 total time=   1.0s
[CV 2/5] END ................C=0.001, gamma=100;, score=0.590 total time=   0.8s
[CV 3/5] END ................C=0.001, gamma=100;, score=0.590 total time=   0.8s
[CV 4/5] END ................C=0.001, gamma=100;, score=0.591 total time=   0.8s
[CV 5/5] END ................C=0.001, gamma=100;, score=0.591 total time=   0.9s
[CV 1/5] END .................C=0.001, gamma=10;, score=0.592 total time=   0.8s
[CV 2/5] END .................C=0.001, gamma=10;, score=0.590 total time=   0.9s
[CV 3/5] END .................C=0.001, gamma=10;, score=0.590 total time=   0.9s
[CV 4/5] END .................C=0.001, gamma=10;, score=0.591 total time=   1.0s
[CV 5/5] END .................C=0.001, gamma=10;, score=0.591 total time=   1.0s
[CV 1/5] END ..................C=0.001, gamma=1;, score=0.592 total time=   0.6s
[CV 2/5] END ..................C=0.001, gamma=1;, score=0.590 total time=   0.6s
[CV 3/5] END ..................C=0.001, gamma=1;, score=0.590 total time=   0.6s
[CV 4/5] END ..................C=0.001, gamma=1;, score=0.591 total time=   0.5s
[CV 5/5] END ..................C=0.001, gamma=1;, score=0.591 total time=   0.6s
[CV 1/5] END ................C=0.001, gamma=0.1;, score=0.592 total time=   0.4s
[CV 2/5] END ................C=0.001, gamma=0.1;, score=0.590 total time=   0.4s
[CV 3/5] END ................C=0.001, gamma=0.1;, score=0.590 total time=   0.4s
[CV 4/5] END ................C=0.001, gamma=0.1;, score=0.591 total time=   0.4s
[CV 5/5] END ................C=0.001, gamma=0.1;, score=0.591 total time=   0.4s
[CV 1/5] END ...............C=0.001, gamma=0.01;, score=0.592 total time=   0.4s
[CV 2/5] END ...............C=0.001, gamma=0.01;, score=0.590 total time=   0.4s
[CV 3/5] END ...............C=0.001, gamma=0.01;, score=0.590 total time=   0.4s
[CV 4/5] END ...............C=0.001, gamma=0.01;, score=0.591 total time=   0.3s
[CV 5/5] END ...............C=0.001, gamma=0.01;, score=0.591 total time=   0.4s
[CV 1/5] END ..............C=0.001, gamma=0.001;, score=0.592 total time=   0.4s
[CV 2/5] END ..............C=0.001, gamma=0.001;, score=0.590 total time=   0.4s
[CV 3/5] END ..............C=0.001, gamma=0.001;, score=0.590 total time=   0.4s
[CV 4/5] END ..............C=0.001, gamma=0.001;, score=0.591 total time=   0.4s
[CV 5/5] END ..............C=0.001, gamma=0.001;, score=0.591 total time=   0.4s
[CV 1/5] END .............C=0.001, gamma=0.0001;, score=0.592 total time=   0.4s
[CV 2/5] END .............C=0.001, gamma=0.0001;, score=0.590 total time=   0.4s
[CV 3/5] END .............C=0.001, gamma=0.0001;, score=0.590 total time=   0.4s
[CV 4/5] END .............C=0.001, gamma=0.0001;, score=0.591 total time=   0.4s
[CV 5/5] END .............C=0.001, gamma=0.0001;, score=0.591 total time=   0.4s
[CV 1/5] END .................C=0.01, gamma=100;, score=0.592 total time=   0.8s
[CV 2/5] END .................C=0.01, gamma=100;, score=0.590 total time=   0.9s
[CV 3/5] END .................C=0.01, gamma=100;, score=0.590 total time=   0.8s
[CV 4/5] END .................C=0.01, gamma=100;, score=0.591 total time=   0.8s
[CV 5/5] END .................C=0.01, gamma=100;, score=0.591 total time=   0.8s
[CV 1/5] END ..................C=0.01, gamma=10;, score=0.592 total time=   0.8s
[CV 2/5] END ..................C=0.01, gamma=10;, score=0.590 total time=   0.8s
[CV 3/5] END ..................C=0.01, gamma=10;, score=0.590 total time=   0.8s
[CV 4/5] END ..................C=0.01, gamma=10;, score=0.591 total time=   0.8s
[CV 5/5] END ..................C=0.01, gamma=10;, score=0.591 total time=   0.8s
[CV 1/5] END ...................C=0.01, gamma=1;, score=0.592 total time=   0.7s
[CV 2/5] END ...................C=0.01, gamma=1;, score=0.590 total time=   0.6s
[CV 3/5] END ...................C=0.01, gamma=1;, score=0.590 total time=   0.6s
[CV 4/5] END ...................C=0.01, gamma=1;, score=0.591 total time=   0.6s
[CV 5/5] END ...................C=0.01, gamma=1;, score=0.591 total time=   0.6s
[CV 1/5] END .................C=0.01, gamma=0.1;, score=0.592 total time=   0.4s
[CV 2/5] END .................C=0.01, gamma=0.1;, score=0.590 total time=   0.4s
[CV 3/5] END .................C=0.01, gamma=0.1;, score=0.590 total time=   0.4s
[CV 4/5] END .................C=0.01, gamma=0.1;, score=0.591 total time=   0.4s
[CV 5/5] END .................C=0.01, gamma=0.1;, score=0.591 total time=   0.4s
[CV 1/5] END ................C=0.01, gamma=0.01;, score=0.592 total time=   0.3s
[CV 2/5] END ................C=0.01, gamma=0.01;, score=0.590 total time=   0.3s
[CV 3/5] END ................C=0.01, gamma=0.01;, score=0.590 total time=   0.3s
[CV 4/5] END ................C=0.01, gamma=0.01;, score=0.591 total time=   0.3s
[CV 5/5] END ................C=0.01, gamma=0.01;, score=0.591 total time=   0.3s
[CV 1/5] END ...............C=0.01, gamma=0.001;, score=0.592 total time=   0.3s
[CV 2/5] END ...............C=0.01, gamma=0.001;, score=0.590 total time=   0.3s
[CV 3/5] END ...............C=0.01, gamma=0.001;, score=0.590 total time=   0.3s
[CV 4/5] END ...............C=0.01, gamma=0.001;, score=0.591 total time=   0.3s
[CV 5/5] END ...............C=0.01, gamma=0.001;, score=0.591 total time=   0.3s
[CV 1/5] END ..............C=0.01, gamma=0.0001;, score=0.592 total time=   0.3s
[CV 2/5] END ..............C=0.01, gamma=0.0001;, score=0.590 total time=   0.3s
[CV 3/5] END ..............C=0.01, gamma=0.0001;, score=0.590 total time=   0.3s
[CV 4/5] END ..............C=0.01, gamma=0.0001;, score=0.591 total time=   0.3s
[CV 5/5] END ..............C=0.01, gamma=0.0001;, score=0.591 total time=   0.3s
[CV 1/5] END ..................C=0.1, gamma=100;, score=0.592 total time=   0.8s
[CV 2/5] END ..................C=0.1, gamma=100;, score=0.590 total time=   0.8s
[CV 3/5] END ..................C=0.1, gamma=100;, score=0.590 total time=   0.8s
[CV 4/5] END ..................C=0.1, gamma=100;, score=0.591 total time=   0.7s
[CV 5/5] END ..................C=0.1, gamma=100;, score=0.591 total time=   0.8s
[CV 1/5] END ...................C=0.1, gamma=10;, score=0.592 total time=   0.8s
[CV 2/5] END ...................C=0.1, gamma=10;, score=0.590 total time=   0.7s
[CV 3/5] END ...................C=0.1, gamma=10;, score=0.590 total time=   0.7s
[CV 4/5] END ...................C=0.1, gamma=10;, score=0.591 total time=   0.8s
[CV 5/5] END ...................C=0.1, gamma=10;, score=0.591 total time=   0.8s
[CV 1/5] END ....................C=0.1, gamma=1;, score=0.592 total time=   0.6s
[CV 2/5] END ....................C=0.1, gamma=1;, score=0.590 total time=   0.6s
[CV 3/5] END ....................C=0.1, gamma=1;, score=0.590 total time=   0.6s
[CV 4/5] END ....................C=0.1, gamma=1;, score=0.591 total time=   0.6s
[CV 5/5] END ....................C=0.1, gamma=1;, score=0.591 total time=   0.6s
[CV 1/5] END ..................C=0.1, gamma=0.1;, score=0.595 total time=   0.4s
[CV 2/5] END ..................C=0.1, gamma=0.1;, score=0.590 total time=   0.4s
[CV 3/5] END ..................C=0.1, gamma=0.1;, score=0.593 total time=   0.4s
[CV 4/5] END ..................C=0.1, gamma=0.1;, score=0.593 total time=   0.4s
[CV 5/5] END ..................C=0.1, gamma=0.1;, score=0.594 total time=   0.4s
[CV 1/5] END .................C=0.1, gamma=0.01;, score=0.751 total time=   0.3s
[CV 2/5] END .................C=0.1, gamma=0.01;, score=0.803 total time=   0.3s
[CV 3/5] END .................C=0.1, gamma=0.01;, score=0.774 total time=   0.3s
[CV 4/5] END .................C=0.1, gamma=0.01;, score=0.787 total time=   0.3s
[CV 5/5] END .................C=0.1, gamma=0.01;, score=0.791 total time=   0.3s
[CV 1/5] END ................C=0.1, gamma=0.001;, score=0.762 total time=   0.3s
[CV 2/5] END ................C=0.1, gamma=0.001;, score=0.795 total time=   0.3s
[CV 3/5] END ................C=0.1, gamma=0.001;, score=0.761 total time=   0.3s
[CV 4/5] END ................C=0.1, gamma=0.001;, score=0.791 total time=   0.3s
[CV 5/5] END ................C=0.1, gamma=0.001;, score=0.775 total time=   0.3s
[CV 1/5] END ...............C=0.1, gamma=0.0001;, score=0.592 total time=   0.3s
[CV 2/5] END ...............C=0.1, gamma=0.0001;, score=0.590 total time=   0.3s
[CV 3/5] END ...............C=0.1, gamma=0.0001;, score=0.590 total time=   0.3s
[CV 4/5] END ...............C=0.1, gamma=0.0001;, score=0.591 total time=   0.3s
[CV 5/5] END ...............C=0.1, gamma=0.0001;, score=0.591 total time=   0.3s
[CV 1/5] END ....................C=1, gamma=100;, score=0.597 total time=   0.8s
[CV 2/5] END ....................C=1, gamma=100;, score=0.597 total time=   0.8s
[CV 3/5] END ....................C=1, gamma=100;, score=0.593 total time=   0.8s
[CV 4/5] END ....................C=1, gamma=100;, score=0.594 total time=   0.8s
[CV 5/5] END ....................C=1, gamma=100;, score=0.594 total time=   0.8s
[CV 1/5] END .....................C=1, gamma=10;, score=0.597 total time=   0.8s
[CV 2/5] END .....................C=1, gamma=10;, score=0.597 total time=   0.8s
[CV 3/5] END .....................C=1, gamma=10;, score=0.593 total time=   0.8s
[CV 4/5] END .....................C=1, gamma=10;, score=0.594 total time=   0.8s
[CV 5/5] END .....................C=1, gamma=10;, score=0.594 total time=   0.8s
[CV 1/5] END ......................C=1, gamma=1;, score=0.602 total time=   0.7s
[CV 2/5] END ......................C=1, gamma=1;, score=0.598 total time=   0.6s
[CV 3/5] END ......................C=1, gamma=1;, score=0.595 total time=   0.6s
[CV 4/5] END ......................C=1, gamma=1;, score=0.596 total time=   0.6s
[CV 5/5] END ......................C=1, gamma=1;, score=0.594 total time=   0.6s
[CV 1/5] END ....................C=1, gamma=0.1;, score=0.779 total time=   0.4s
[CV 2/5] END ....................C=1, gamma=0.1;, score=0.833 total time=   0.4s
[CV 3/5] END ....................C=1, gamma=0.1;, score=0.780 total time=   0.4s
[CV 4/5] END ....................C=1, gamma=0.1;, score=0.795 total time=   0.4s
[CV 5/5] END ....................C=1, gamma=0.1;, score=0.780 total time=   0.4s
[CV 1/5] END ...................C=1, gamma=0.01;, score=0.805 total time=   0.2s
[CV 2/5] END ...................C=1, gamma=0.01;, score=0.848 total time=   0.2s
[CV 3/5] END ...................C=1, gamma=0.01;, score=0.808 total time=   0.2s
[CV 4/5] END ...................C=1, gamma=0.01;, score=0.826 total time=   0.2s
[CV 5/5] END ...................C=1, gamma=0.01;, score=0.828 total time=   0.2s
[CV 1/5] END ..................C=1, gamma=0.001;, score=0.798 total time=   0.2s
[CV 2/5] END ..................C=1, gamma=0.001;, score=0.838 total time=   0.2s
[CV 3/5] END ..................C=1, gamma=0.001;, score=0.779 total time=   0.2s
[CV 4/5] END ..................C=1, gamma=0.001;, score=0.811 total time=   0.2s
[CV 5/5] END ..................C=1, gamma=0.001;, score=0.823 total time=   0.3s
[CV 1/5] END .................C=1, gamma=0.0001;, score=0.779 total time=   0.3s
[CV 2/5] END .................C=1, gamma=0.0001;, score=0.816 total time=   0.3s
[CV 3/5] END .................C=1, gamma=0.0001;, score=0.759 total time=   0.3s
[CV 4/5] END .................C=1, gamma=0.0001;, score=0.782 total time=   0.3s
[CV 5/5] END .................C=1, gamma=0.0001;, score=0.803 total time=   0.3s
[CV 1/5] END ...................C=10, gamma=100;, score=0.597 total time=   0.8s
[CV 2/5] END ...................C=10, gamma=100;, score=0.597 total time=   0.8s
[CV 3/5] END ...................C=10, gamma=100;, score=0.593 total time=   0.8s
[CV 4/5] END ...................C=10, gamma=100;, score=0.594 total time=   0.9s
[CV 5/5] END ...................C=10, gamma=100;, score=0.594 total time=   0.8s
[CV 1/5] END ....................C=10, gamma=10;, score=0.597 total time=   0.8s
[CV 2/5] END ....................C=10, gamma=10;, score=0.597 total time=   0.8s
[CV 3/5] END ....................C=10, gamma=10;, score=0.593 total time=   0.8s
[CV 4/5] END ....................C=10, gamma=10;, score=0.594 total time=   0.8s
[CV 5/5] END ....................C=10, gamma=10;, score=0.594 total time=   0.8s
[CV 1/5] END .....................C=10, gamma=1;, score=0.607 total time=   0.7s
[CV 2/5] END .....................C=10, gamma=1;, score=0.600 total time=   0.7s
[CV 3/5] END .....................C=10, gamma=1;, score=0.595 total time=   0.7s
[CV 4/5] END .....................C=10, gamma=1;, score=0.596 total time=   0.7s
[CV 5/5] END .....................C=10, gamma=1;, score=0.594 total time=   0.8s
[CV 1/5] END ...................C=10, gamma=0.1;, score=0.785 total time=   0.5s
[CV 2/5] END ...................C=10, gamma=0.1;, score=0.828 total time=   0.5s
[CV 3/5] END ...................C=10, gamma=0.1;, score=0.789 total time=   0.5s
[CV 4/5] END ...................C=10, gamma=0.1;, score=0.808 total time=   0.5s
[CV 5/5] END ...................C=10, gamma=0.1;, score=0.770 total time=   0.5s
[CV 1/5] END ..................C=10, gamma=0.01;, score=0.841 total time=   0.2s
[CV 2/5] END ..................C=10, gamma=0.01;, score=0.872 total time=   0.3s
[CV 3/5] END ..................C=10, gamma=0.01;, score=0.852 total time=   0.2s
[CV 4/5] END ..................C=10, gamma=0.01;, score=0.865 total time=   0.3s
[CV 5/5] END ..................C=10, gamma=0.01;, score=0.865 total time=   0.3s
[CV 1/5] END .................C=10, gamma=0.001;, score=0.843 total time=   0.2s
[CV 2/5] END .................C=10, gamma=0.001;, score=0.892 total time=   0.2s
[CV 3/5] END .................C=10, gamma=0.001;, score=0.849 total time=   0.2s
[CV 4/5] END .................C=10, gamma=0.001;, score=0.877 total time=   0.2s
[CV 5/5] END .................C=10, gamma=0.001;, score=0.880 total time=   0.2s
[CV 1/5] END ................C=10, gamma=0.0001;, score=0.813 total time=   0.2s
[CV 2/5] END ................C=10, gamma=0.0001;, score=0.862 total time=   0.2s
[CV 3/5] END ................C=10, gamma=0.0001;, score=0.787 total time=   0.2s
[CV 4/5] END ................C=10, gamma=0.0001;, score=0.831 total time=   0.2s
[CV 5/5] END ................C=10, gamma=0.0001;, score=0.841 total time=   0.3s
[CV 1/5] END ..................C=100, gamma=100;, score=0.597 total time=   0.9s
[CV 2/5] END ..................C=100, gamma=100;, score=0.597 total time=   1.0s
[CV 3/5] END ..................C=100, gamma=100;, score=0.593 total time=   1.0s
[CV 4/5] END ..................C=100, gamma=100;, score=0.594 total time=   0.9s
[CV 5/5] END ..................C=100, gamma=100;, score=0.594 total time=   0.9s
[CV 1/5] END ...................C=100, gamma=10;, score=0.597 total time=   0.8s
[CV 2/5] END ...................C=100, gamma=10;, score=0.597 total time=   0.9s
[CV 3/5] END ...................C=100, gamma=10;, score=0.593 total time=   0.8s
[CV 4/5] END ...................C=100, gamma=10;, score=0.594 total time=   0.8s
[CV 5/5] END ...................C=100, gamma=10;, score=0.594 total time=   0.9s
[CV 1/5] END ....................C=100, gamma=1;, score=0.607 total time=   0.8s
[CV 2/5] END ....................C=100, gamma=1;, score=0.600 total time=   0.7s
[CV 3/5] END ....................C=100, gamma=1;, score=0.595 total time=   0.7s
[CV 4/5] END ....................C=100, gamma=1;, score=0.596 total time=   0.9s
[CV 5/5] END ....................C=100, gamma=1;, score=0.594 total time=   0.7s
[CV 1/5] END ..................C=100, gamma=0.1;, score=0.784 total time=   0.5s
[CV 2/5] END ..................C=100, gamma=0.1;, score=0.828 total time=   0.5s
[CV 3/5] END ..................C=100, gamma=0.1;, score=0.789 total time=   0.5s
[CV 4/5] END ..................C=100, gamma=0.1;, score=0.808 total time=   0.5s
[CV 5/5] END ..................C=100, gamma=0.1;, score=0.770 total time=   0.5s
[CV 1/5] END .................C=100, gamma=0.01;, score=0.803 total time=   0.4s
[CV 2/5] END .................C=100, gamma=0.01;, score=0.823 total time=   0.5s
[CV 3/5] END .................C=100, gamma=0.01;, score=0.823 total time=   0.4s
[CV 4/5] END .................C=100, gamma=0.01;, score=0.829 total time=   0.4s
[CV 5/5] END .................C=100, gamma=0.01;, score=0.836 total time=   0.4s
[CV 1/5] END ................C=100, gamma=0.001;, score=0.843 total time=   0.3s
[CV 2/5] END ................C=100, gamma=0.001;, score=0.892 total time=   0.3s
[CV 3/5] END ................C=100, gamma=0.001;, score=0.849 total time=   0.3s
[CV 4/5] END ................C=100, gamma=0.001;, score=0.878 total time=   0.3s
[CV 5/5] END ................C=100, gamma=0.001;, score=0.878 total time=   0.3s
[CV 1/5] END ...............C=100, gamma=0.0001;, score=0.843 total time=   0.3s
[CV 2/5] END ...............C=100, gamma=0.0001;, score=0.892 total time=   0.3s
[CV 3/5] END ...............C=100, gamma=0.0001;, score=0.849 total time=   0.3s
[CV 4/5] END ...............C=100, gamma=0.0001;, score=0.877 total time=   0.3s
[CV 5/5] END ...............C=100, gamma=0.0001;, score=0.882 total time=   0.3s
[CV 1/5] END .................C=1000, gamma=100;, score=0.597 total time=   0.9s
[CV 2/5] END .................C=1000, gamma=100;, score=0.597 total time=   0.8s
[CV 3/5] END .................C=1000, gamma=100;, score=0.593 total time=   0.8s
[CV 4/5] END .................C=1000, gamma=100;, score=0.594 total time=   1.1s
[CV 5/5] END .................C=1000, gamma=100;, score=0.594 total time=   0.9s
[CV 1/5] END ..................C=1000, gamma=10;, score=0.597 total time=   0.9s
[CV 2/5] END ..................C=1000, gamma=10;, score=0.597 total time=   1.1s
[CV 3/5] END ..................C=1000, gamma=10;, score=0.593 total time=   0.9s
[CV 4/5] END ..................C=1000, gamma=10;, score=0.594 total time=   0.9s
[CV 5/5] END ..................C=1000, gamma=10;, score=0.594 total time=   0.9s
[CV 1/5] END ...................C=1000, gamma=1;, score=0.607 total time=   0.8s
[CV 2/5] END ...................C=1000, gamma=1;, score=0.600 total time=   0.7s
[CV 3/5] END ...................C=1000, gamma=1;, score=0.595 total time=   0.7s
[CV 4/5] END ...................C=1000, gamma=1;, score=0.596 total time=   0.7s
[CV 5/5] END ...................C=1000, gamma=1;, score=0.594 total time=   0.7s
[CV 1/5] END .................C=1000, gamma=0.1;, score=0.784 total time=   0.5s
[CV 2/5] END .................C=1000, gamma=0.1;, score=0.828 total time=   0.5s
[CV 3/5] END .................C=1000, gamma=0.1;, score=0.789 total time=   0.5s
[CV 4/5] END .................C=1000, gamma=0.1;, score=0.808 total time=   0.5s
[CV 5/5] END .................C=1000, gamma=0.1;, score=0.770 total time=   0.5s
[CV 1/5] END ................C=1000, gamma=0.01;, score=0.795 total time=   0.7s
[CV 2/5] END ................C=1000, gamma=0.01;, score=0.803 total time=   0.9s
[CV 3/5] END ................C=1000, gamma=0.01;, score=0.784 total time=   0.6s
[CV 4/5] END ................C=1000, gamma=0.01;, score=0.801 total time=   0.8s
[CV 5/5] END ................C=1000, gamma=0.01;, score=0.803 total time=   0.8s
[CV 1/5] END ...............C=1000, gamma=0.001;, score=0.849 total time=   0.8s
[CV 2/5] END ...............C=1000, gamma=0.001;, score=0.890 total time=   1.2s
[CV 3/5] END ...............C=1000, gamma=0.001;, score=0.861 total time=   0.9s
[CV 4/5] END ...............C=1000, gamma=0.001;, score=0.875 total time=   0.8s
[CV 5/5] END ...............C=1000, gamma=0.001;, score=0.874 total time=   0.8s
[CV 1/5] END ..............C=1000, gamma=0.0001;, score=0.843 total time=   0.8s
[CV 2/5] END ..............C=1000, gamma=0.0001;, score=0.892 total time=   0.8s
[CV 3/5] END ..............C=1000, gamma=0.0001;, score=0.849 total time=   0.9s
[CV 4/5] END ..............C=1000, gamma=0.0001;, score=0.877 total time=   0.9s
[CV 5/5] END ..............C=1000, gamma=0.0001;, score=0.882 total time=   0.7s

GridSearchCV(estimator=SVC(random_state=101),
             param_grid={'C': [0.001, 0.01, 0.1, 1, 10, 100, 1000],
                         'gamma': [100, 10, 1, 0.1, 0.01, 0.001, 0.0001]},
             verbose=3)

GridSearchCV(estimator=SVC(random_state=101),
             param_grid={'C': [0.001, 0.01, 0.1, 1, 10, 100, 1000],
                         'gamma': [100, 10, 1, 0.1, 0.01, 0.001, 0.0001]},
             verbose=3)

SVC(random_state=101)

SVC(random_state=101)


grid3.best_params_

{'C': 1000, 'gamma': 0.001}


pred = grid3.predict(X_valid)


print('Score:\n',grid3.score(X_valid,y_valid),'\n')

from sklearn.metrics import confusion_matrix
print('Confusion Matrix:\n',confusion_matrix(y_valid,pred),'\n')

from sklearn.metrics import classification_report
print('Classification Report:\n',classification_report(y_valid,pred))

Score:
 0.8620689655172413 

Confusion Matrix:
 [[216  42]
 [ 18 159]] 

Classification Report:
               precision    recall  f1-score   support

           0       0.92      0.84      0.88       258
           1       0.79      0.90      0.84       177

    accuracy                           0.86       435
   macro avg       0.86      0.87      0.86       435
weighted avg       0.87      0.86      0.86       435


mat_T = confusion_matrix(y_valid,pred)
sns.heatmap(mat_T, square=True, annot=True,fmt='d',cbar=False)
plt.xlabel('true label')
plt.ylabel('predicted label');


df_eval = pd.DataFrame({'Label':y_valid,
                        'Prediction': pred
                        })
df_eval


df_eval[df_eval['Label'] != df_eval['Prediction']]


from sklearn.naive_bayes import GaussianNB


gnb = GaussianNB()


gnb.fit(X_train, y_train)

GaussianNB()

GaussianNB()


pred = gnb.predict(X_valid)


print("Number of mislabeled points out of a total %d points : %d" % (X_valid.shape[0], (y_valid != pred).sum()))

Number of mislabeled points out of a total 435 points : 56


print('Score:\n',gnb.score(X_valid,y_valid),'\n')

from sklearn.metrics import confusion_matrix
print('Confusion Matrix:\n',confusion_matrix(y_valid,pred),'\n')

from sklearn.metrics import classification_report
print('Classification Report:\n',classification_report(y_valid,pred))

Score:
 0.871264367816092 

Confusion Matrix:
 [[224  34]
 [ 22 155]] 

Classification Report:
               precision    recall  f1-score   support

           0       0.91      0.87      0.89       258
           1       0.82      0.88      0.85       177

    accuracy                           0.87       435
   macro avg       0.87      0.87      0.87       435
weighted avg       0.87      0.87      0.87       435


mat_T = confusion_matrix(y_valid,pred)
sns.heatmap(mat_T, square=True, annot=True,fmt='d',cbar=False)
plt.xlabel('true label')
plt.ylabel('predicted label');


from sklearn.naive_bayes import BernoulliNB
bnb = BernoulliNB()
bnb.fit(X_train, y_train)
pred = bnb.predict(X_valid)


print("Number of mislabeled points out of a total %d points : %d" % (X_valid.shape[0], (y_valid != pred).sum()))

Number of mislabeled points out of a total 435 points : 128


print('Score:\n',bnb.score(X_valid,y_valid),'\n')

from sklearn.metrics import confusion_matrix
print('Confusion Matrix:\n',confusion_matrix(y_valid,pred),'\n')

from sklearn.metrics import classification_report
print('Classification Report:\n',classification_report(y_valid,pred))

Score:
 0.7057471264367816 

Confusion Matrix:
 [[208  50]
 [ 78  99]] 

Classification Report:
               precision    recall  f1-score   support

           0       0.73      0.81      0.76       258
           1       0.66      0.56      0.61       177

    accuracy                           0.71       435
   macro avg       0.70      0.68      0.69       435
weighted avg       0.70      0.71      0.70       435


mat_T = confusion_matrix(y_valid,pred)
sns.heatmap(mat_T, square=True, annot=True,fmt='d',cbar=False)
plt.xlabel('true label')
plt.ylabel('predicted label');


param_grid = {'alpha':[100,50,10,1,0.1,0.001,0.0001]}


from sklearn.model_selection import GridSearchCV
grid4 = GridSearchCV(BernoulliNB(),param_grid,refit=True,verbose=3)
grid4.fit(X_train,y_train)
pred = grid4.predict(X_valid)

Fitting 5 folds for each of 7 candidates, totalling 35 fits
[CV 1/5] END .........................alpha=100;, score=0.697 total time=   0.0s
[CV 2/5] END .........................alpha=100;, score=0.708 total time=   0.0s
[CV 3/5] END .........................alpha=100;, score=0.711 total time=   0.0s
[CV 4/5] END .........................alpha=100;, score=0.686 total time=   0.0s
[CV 5/5] END .........................alpha=100;, score=0.713 total time=   0.0s
[CV 1/5] END ..........................alpha=50;, score=0.693 total time=   0.0s
[CV 2/5] END ..........................alpha=50;, score=0.698 total time=   0.0s
[CV 3/5] END ..........................alpha=50;, score=0.702 total time=   0.0s
[CV 4/5] END ..........................alpha=50;, score=0.677 total time=   0.0s
[CV 5/5] END ..........................alpha=50;, score=0.721 total time=   0.0s
[CV 1/5] END ..........................alpha=10;, score=0.710 total time=   0.0s
[CV 2/5] END ..........................alpha=10;, score=0.708 total time=   0.0s
[CV 3/5] END ..........................alpha=10;, score=0.703 total time=   0.0s
[CV 4/5] END ..........................alpha=10;, score=0.678 total time=   0.0s
[CV 5/5] END ..........................alpha=10;, score=0.726 total time=   0.0s
[CV 1/5] END ...........................alpha=1;, score=0.707 total time=   0.0s
[CV 2/5] END ...........................alpha=1;, score=0.713 total time=   0.0s
[CV 3/5] END ...........................alpha=1;, score=0.702 total time=   0.0s
[CV 4/5] END ...........................alpha=1;, score=0.681 total time=   0.0s
[CV 5/5] END ...........................alpha=1;, score=0.724 total time=   0.0s
[CV 1/5] END .........................alpha=0.1;, score=0.707 total time=   0.0s
[CV 2/5] END .........................alpha=0.1;, score=0.713 total time=   0.0s
[CV 3/5] END .........................alpha=0.1;, score=0.702 total time=   0.0s
[CV 4/5] END .........................alpha=0.1;, score=0.686 total time=   0.0s
[CV 5/5] END .........................alpha=0.1;, score=0.724 total time=   0.0s
[CV 1/5] END .......................alpha=0.001;, score=0.707 total time=   0.0s
[CV 2/5] END .......................alpha=0.001;, score=0.713 total time=   0.0s
[CV 3/5] END .......................alpha=0.001;, score=0.702 total time=   0.0s
[CV 4/5] END .......................alpha=0.001;, score=0.686 total time=   0.0s
[CV 5/5] END .......................alpha=0.001;, score=0.724 total time=   0.0s
[CV 1/5] END ......................alpha=0.0001;, score=0.707 total time=   0.0s
[CV 2/5] END ......................alpha=0.0001;, score=0.713 total time=   0.0s
[CV 3/5] END ......................alpha=0.0001;, score=0.702 total time=   0.0s
[CV 4/5] END ......................alpha=0.0001;, score=0.686 total time=   0.0s
[CV 5/5] END ......................alpha=0.0001;, score=0.724 total time=   0.0s


grid4.best_params_

{'alpha': 0.1}


print('Score:\n',grid4.score(X_valid,y_valid),'\n')

from sklearn.metrics import confusion_matrix
print('Confusion Matrix:\n',confusion_matrix(y_valid,pred),'\n')

from sklearn.metrics import classification_report
print('Classification Report:\n',classification_report(y_valid,pred))

Score:
 0.7057471264367816 

Confusion Matrix:
 [[208  50]
 [ 78  99]] 

Classification Report:
               precision    recall  f1-score   support

           0       0.73      0.81      0.76       258
           1       0.66      0.56      0.61       177

    accuracy                           0.71       435
   macro avg       0.70      0.68      0.69       435
weighted avg       0.70      0.71      0.70       435


mat_T = confusion_matrix(y_valid,pred)
sns.heatmap(mat_T, square=True, annot=True,fmt='d',cbar=False)
plt.xlabel('true label')
plt.ylabel('predicted label');


from sklearn.ensemble import BaggingClassifier


bca = BaggingClassifier(RandomForestClassifier(criterion='entropy', n_estimators=150,random_state=101),random_state=101)


bca.fit(X_train, y_train)

BaggingClassifier(estimator=RandomForestClassifier(criterion='entropy',
                                                   n_estimators=150,
                                                   random_state=101),
                  random_state=101)

BaggingClassifier(estimator=RandomForestClassifier(criterion='entropy',
                                                   n_estimators=150,
                                                   random_state=101),
                  random_state=101)

RandomForestClassifier(criterion='entropy', n_estimators=150, random_state=101)

RandomForestClassifier(criterion='entropy', n_estimators=150, random_state=101)


pred = bca.predict(X_valid)


print('Score:\n',bca.score(X_valid,y_valid),'\n')

from sklearn.metrics import confusion_matrix
print('Confusion Matrix:\n',confusion_matrix(y_valid,pred),'\n')

from sklearn.metrics import classification_report
print('Classification Report:\n',classification_report(y_valid,pred))

Score:
 0.8850574712643678 

Confusion Matrix:
 [[224  34]
 [ 16 161]] 

Classification Report:
               precision    recall  f1-score   support

           0       0.93      0.87      0.90       258
           1       0.83      0.91      0.87       177

    accuracy                           0.89       435
   macro avg       0.88      0.89      0.88       435
weighted avg       0.89      0.89      0.89       435


mat_T = confusion_matrix(y_valid,pred)
sns.heatmap(mat_T, square=True, annot=True, fmt='d', cbar=False)
plt.xlabel('true label')
plt.ylabel('predicted label');


from sklearn.ensemble import GradientBoostingClassifier


gbc = GradientBoostingClassifier(random_state=101)


gbc.fit(X_train, y_train)

GradientBoostingClassifier(random_state=101)

GradientBoostingClassifier(random_state=101)


pred = gbc.predict(X_valid)


print('Score:\n',gbc.score(X_valid,y_valid),'\n')

from sklearn.metrics import confusion_matrix
print('Confusion Matrix:\n',confusion_matrix(y_valid,pred),'\n')

from sklearn.metrics import classification_report
print('Classification Report:\n',classification_report(y_valid,pred))

Score:
 0.8919540229885058 

Confusion Matrix:
 [[224  34]
 [ 13 164]] 

Classification Report:
               precision    recall  f1-score   support

           0       0.95      0.87      0.91       258
           1       0.83      0.93      0.87       177

    accuracy                           0.89       435
   macro avg       0.89      0.90      0.89       435
weighted avg       0.90      0.89      0.89       435


mat_T = confusion_matrix(y_valid,pred)
sns.heatmap(mat_T, square=True, annot=True, fmt='d', cbar=False)
plt.xlabel('true label')
plt.ylabel('predicted label');


from xgboost import XGBClassifier


from sklearn.metrics import accuracy_score


xgb = XGBClassifier()


xgb.fit(X_train, y_train)

XGBClassifier(base_score=None, booster=None, callbacks=None,
              colsample_bylevel=None, colsample_bynode=None,
              colsample_bytree=None, early_stopping_rounds=None,
              enable_categorical=False, eval_metric=None, feature_types=None,
              gamma=None, gpu_id=None, grow_policy=None, importance_type=None,
              interaction_constraints=None, learning_rate=None, max_bin=None,
              max_cat_threshold=None, max_cat_to_onehot=None,
              max_delta_step=None, max_depth=None, max_leaves=None,
              min_child_weight=None, missing=nan, monotone_constraints=None,
              n_estimators=100, n_jobs=None, num_parallel_tree=None,
              predictor=None, random_state=None, ...)

XGBClassifier(base_score=None, booster=None, callbacks=None,
              colsample_bylevel=None, colsample_bynode=None,
              colsample_bytree=None, early_stopping_rounds=None,
              enable_categorical=False, eval_metric=None, feature_types=None,
              gamma=None, gpu_id=None, grow_policy=None, importance_type=None,
              interaction_constraints=None, learning_rate=None, max_bin=None,
              max_cat_threshold=None, max_cat_to_onehot=None,
              max_delta_step=None, max_depth=None, max_leaves=None,
              min_child_weight=None, missing=nan, monotone_constraints=None,
              n_estimators=100, n_jobs=None, num_parallel_tree=None,
              predictor=None, random_state=None, ...)


pred = xgb.predict(X_valid)


predictions = [round(value) for value in pred]


accuracy = accuracy_score(y_valid, predictions)
print("Accuracy: %.3f%%" % (accuracy * 100.0))

Accuracy: 86.897%


print('Score:\n',xgb.score(X_valid,y_valid),'\n')

from sklearn.metrics import confusion_matrix
print('Confusion Matrix:\n',confusion_matrix(y_valid,pred),'\n')

from sklearn.metrics import classification_report
print('Classification Report:\n',classification_report(y_valid,pred))

Score:
 0.8689655172413793 

Confusion Matrix:
 [[227  31]
 [ 26 151]] 

Classification Report:
               precision    recall  f1-score   support

           0       0.90      0.88      0.89       258
           1       0.83      0.85      0.84       177

    accuracy                           0.87       435
   macro avg       0.86      0.87      0.86       435
weighted avg       0.87      0.87      0.87       435


mat_T = confusion_matrix(y_valid,pred)
sns.heatmap(mat_T, square=True, annot=True, fmt='d', cbar=False)
plt.xlabel('true label')
plt.ylabel('predicted label');


from sklearn.ensemble import VotingClassifier


model_1 = GradientBoostingClassifier(random_state=101)
model_1.fit(X_train, y_train)
pred_1 = model_1.predict(X_valid)


model_2 = RandomForestClassifier(criterion='entropy',n_estimators=150,random_state=101)
model_2.fit(X_train, y_train)
pred_2 = model_2.predict(X_valid)


model_3 = GaussianNB()
model_3.fit(X_train, y_train)
pred_3 = model_3.predict(X_valid)


eclf = VotingClassifier(estimators=[('Gradient Boosting', model_1),
                                    ('Random Forest', model_2),
                                    ('Gaussian Naive Bayes', model_3)]
                        ,voting='hard')


from sklearn.model_selection import cross_val_score


for clf, label in zip([model_1, model_2, model_3, eclf], ['Gradient Boosting', 'Random Forest', 'Gaussian Naive Bayes', 
                                                          'Ensemble']):

    scores = cross_val_score(clf, X_valid, y_valid, scoring='accuracy', cv=5)

    print("Accuracy: %0.3f (+/- %0.3f) [%s]" % (scores.mean(), scores.std(), label))

Accuracy: 0.880 (+/- 0.044) [Gradient Boosting]
Accuracy: 0.876 (+/- 0.038) [Random Forest]
Accuracy: 0.818 (+/- 0.061) [Gaussian Naive Bayes]
Accuracy: 0.878 (+/- 0.042) [Ensemble]


X_trainValid = pd.concat([X_train,X_valid],axis=0)


X_trainValid


y_trainValid = pd.concat([y_train,y_valid],axis=0)


y_trainValid

3567    0
3483    0
3588    0
335     1
2910    0
       ..
2241    1
2258    1
2047    1
72      0
2116    0
Name: Degree of Injury_Nonfatal, Length: 3483, dtype: uint8


model_0 = LogisticRegression(random_state=101)
model_0.fit(X_trainValid, y_trainValid)

LogisticRegression(random_state=101)

LogisticRegression(random_state=101)


from sklearn.model_selection import ShuffleSplit
n_samples = X_trainValid.shape[0]
cv = ShuffleSplit(n_splits=5, test_size=0.3, random_state=101)
scores = cross_val_score(model_0, X_trainValid, y_trainValid, cv=cv)
print(scores)
print("%0.3f accuracy with a standard deviation of %0.3f" % (scores.mean(), scores.std()))

[0.78373206 0.77511962 0.80956938 0.80191388 0.77799043]
0.790 accuracy with a standard deviation of 0.014


pred_0 = model_0.predict(X_test)


print('Score:\n',model_0.score(X_test,y_test),'\n')

from sklearn.metrics import confusion_matrix
print('Confusion Matrix:\n',confusion_matrix(y_test,pred_0),'\n')

from sklearn.metrics import classification_report
print('Classification Report:\n',classification_report(y_test,pred_0))

Score:
 0.7729357798165137 

Confusion Matrix:
 [[419 112]
 [ 86 255]] 

Classification Report:
               precision    recall  f1-score   support

           0       0.83      0.79      0.81       531
           1       0.69      0.75      0.72       341

    accuracy                           0.77       872
   macro avg       0.76      0.77      0.76       872
weighted avg       0.78      0.77      0.77       872


model_1 = RandomForestClassifier(criterion='entropy',n_estimators=150,random_state=101)
model_1.fit(X_trainValid, y_trainValid)

RandomForestClassifier(criterion='entropy', n_estimators=150, random_state=101)

RandomForestClassifier(criterion='entropy', n_estimators=150, random_state=101)


from sklearn.model_selection import ShuffleSplit
n_samples = X_trainValid.shape[0]
cv = ShuffleSplit(n_splits=5, test_size=0.3, random_state=101)
scores = cross_val_score(model_1, X_trainValid, y_trainValid, cv=cv)
print(scores)
print("%0.3f accuracy with a standard deviation of %0.3f" % (scores.mean(), scores.std()))

[0.88038278 0.87655502 0.88708134 0.89760766 0.86794258]
0.882 accuracy with a standard deviation of 0.010


pred_1 = model_1.predict(X_test)


print('Score:\n',model_1.score(X_test,y_test),'\n')

from sklearn.metrics import confusion_matrix
print('Confusion Matrix:\n',confusion_matrix(y_test,pred_1),'\n')

from sklearn.metrics import classification_report
print('Classification Report:\n',classification_report(y_test,pred_1))

Score:
 0.875 

Confusion Matrix:
 [[463  68]
 [ 41 300]] 

Classification Report:
               precision    recall  f1-score   support

           0       0.92      0.87      0.89       531
           1       0.82      0.88      0.85       341

    accuracy                           0.88       872
   macro avg       0.87      0.88      0.87       872
weighted avg       0.88      0.88      0.88       872


model_2 = XGBClassifier()
model_2.fit(X_trainValid, y_trainValid)

XGBClassifier(base_score=None, booster=None, callbacks=None,
              colsample_bylevel=None, colsample_bynode=None,
              colsample_bytree=None, early_stopping_rounds=None,
              enable_categorical=False, eval_metric=None, feature_types=None,
              gamma=None, gpu_id=None, grow_policy=None, importance_type=None,
              interaction_constraints=None, learning_rate=None, max_bin=None,
              max_cat_threshold=None, max_cat_to_onehot=None,
              max_delta_step=None, max_depth=None, max_leaves=None,
              min_child_weight=None, missing=nan, monotone_constraints=None,
              n_estimators=100, n_jobs=None, num_parallel_tree=None,
              predictor=None, random_state=None, ...)

XGBClassifier(base_score=None, booster=None, callbacks=None,
              colsample_bylevel=None, colsample_bynode=None,
              colsample_bytree=None, early_stopping_rounds=None,
              enable_categorical=False, eval_metric=None, feature_types=None,
              gamma=None, gpu_id=None, grow_policy=None, importance_type=None,
              interaction_constraints=None, learning_rate=None, max_bin=None,
              max_cat_threshold=None, max_cat_to_onehot=None,
              max_delta_step=None, max_depth=None, max_leaves=None,
              min_child_weight=None, missing=nan, monotone_constraints=None,
              n_estimators=100, n_jobs=None, num_parallel_tree=None,
              predictor=None, random_state=None, ...)


from sklearn.model_selection import ShuffleSplit
n_samples = X_trainValid.shape[0]
cv = ShuffleSplit(n_splits=5, test_size=0.3, random_state=101)
scores = cross_val_score(model_2, X_trainValid, y_trainValid, cv=cv)
print(scores)
print("%0.3f accuracy with a standard deviation of %0.3f" % (scores.mean(), scores.std()))

[0.87272727 0.86028708 0.87751196 0.89090909 0.86124402]
0.873 accuracy with a standard deviation of 0.011


pred_2 = model_2.predict(X_test)


print('Score:\n',model_2.score(X_test,y_test),'\n')

from sklearn.metrics import confusion_matrix
print('Confusion Matrix:\n',confusion_matrix(y_test,pred_2),'\n')

from sklearn.metrics import classification_report
print('Classification Report:\n',classification_report(y_test,pred_2))

Score:
 0.8646788990825688 

Confusion Matrix:
 [[465  66]
 [ 52 289]] 

Classification Report:
               precision    recall  f1-score   support

           0       0.90      0.88      0.89       531
           1       0.81      0.85      0.83       341

    accuracy                           0.86       872
   macro avg       0.86      0.86      0.86       872
weighted avg       0.87      0.86      0.87       872


model_3 = GradientBoostingClassifier(random_state=101)
model_3.fit(X_trainValid, y_trainValid)

GradientBoostingClassifier(random_state=101)

GradientBoostingClassifier(random_state=101)


from sklearn.model_selection import ShuffleSplit
n_samples = X_trainValid.shape[0]
cv = ShuffleSplit(n_splits=5, test_size=0.3, random_state=101)
scores = cross_val_score(model_3, X_trainValid, y_trainValid, cv=cv)
print(scores)
print("%0.3f accuracy with a standard deviation of %0.3f" % (scores.mean(), scores.std()))

[0.88325359 0.87942584 0.89186603 0.90047847 0.8708134 ]
0.885 accuracy with a standard deviation of 0.010


pred_3 = model_3.predict(X_test)


print('Score:\n',model_3.score(X_test,y_test),'\n')

from sklearn.metrics import confusion_matrix
print('Confusion Matrix:\n',confusion_matrix(y_test,pred_3),'\n')

from sklearn.metrics import classification_report
print('Classification Report:\n',classification_report(y_test,pred_3))

Score:
 0.8692660550458715 

Confusion Matrix:
 [[453  78]
 [ 36 305]] 

Classification Report:
               precision    recall  f1-score   support

           0       0.93      0.85      0.89       531
           1       0.80      0.89      0.84       341

    accuracy                           0.87       872
   macro avg       0.86      0.87      0.87       872
weighted avg       0.88      0.87      0.87       872


model_4 = BaggingClassifier()
model_4.fit(X_trainValid, y_trainValid)

BaggingClassifier()

BaggingClassifier()


from sklearn.model_selection import ShuffleSplit
n_samples = X_trainValid.shape[0]
cv = ShuffleSplit(n_splits=5, test_size=0.3, random_state=101)
scores = cross_val_score(model_4, X_trainValid, y_trainValid, cv=cv)
print(scores)
print("%0.3f accuracy with a standard deviation of %0.3f" % (scores.mean(), scores.std()))

[0.85645933 0.86602871 0.86124402 0.86602871 0.86602871]
0.863 accuracy with a standard deviation of 0.004


pred_4 = model_4.predict(X_test)


print('Score:\n',model_4.score(X_test,y_test),'\n')

from sklearn.metrics import confusion_matrix
print('Confusion Matrix:\n',confusion_matrix(y_test,pred_4),'\n')

from sklearn.metrics import classification_report
print('Classification Report:\n',classification_report(y_test,pred_4))

Score:
 0.8658256880733946 

Confusion Matrix:
 [[474  57]
 [ 60 281]] 

Classification Report:
               precision    recall  f1-score   support

           0       0.89      0.89      0.89       531
           1       0.83      0.82      0.83       341

    accuracy                           0.87       872
   macro avg       0.86      0.86      0.86       872
weighted avg       0.87      0.87      0.87       872


model_5 = SVC(C=1000,gamma=0.001,random_state=101)
model_5.fit(X_trainValid, y_trainValid)

SVC(C=1000, gamma=0.001, random_state=101)

SVC(C=1000, gamma=0.001, random_state=101)


from sklearn.model_selection import ShuffleSplit
n_samples = X_trainValid.shape[0]
cv = ShuffleSplit(n_splits=5, test_size=0.3, random_state=101)
scores = cross_val_score(model_5, X_trainValid, y_trainValid, cv=cv)
print(scores)
print("%0.3f accuracy with a standard deviation of %0.3f" % (scores.mean(), scores.std()))

[0.85645933 0.85837321 0.86794258 0.8861244  0.85358852]
0.864 accuracy with a standard deviation of 0.012


pred_5 = model_5.predict(X_test)


print('Score:\n',model_5.score(X_test,y_test),'\n')

from sklearn.metrics import confusion_matrix
print('Confusion Matrix:\n',confusion_matrix(y_test,pred_5),'\n')

from sklearn.metrics import classification_report
print('Classification Report:\n',classification_report(y_test,pred_5))

Score:
 0.8394495412844036 

Confusion Matrix:
 [[434  97]
 [ 43 298]] 

Classification Report:
               precision    recall  f1-score   support

           0       0.91      0.82      0.86       531
           1       0.75      0.87      0.81       341

    accuracy                           0.84       872
   macro avg       0.83      0.85      0.84       872
weighted avg       0.85      0.84      0.84       872


model_6 = DecisionTreeClassifier(criterion='entropy',splitter='best',random_state=101)
model_6.fit(X_trainValid, y_trainValid)

DecisionTreeClassifier(criterion='entropy', random_state=101)

DecisionTreeClassifier(criterion='entropy', random_state=101)


from sklearn.model_selection import ShuffleSplit
n_samples = X_trainValid.shape[0]
cv = ShuffleSplit(n_splits=5, test_size=0.3, random_state=101)
scores = cross_val_score(model_6, X_trainValid, y_trainValid, cv=cv)
print(scores)
print("%0.3f accuracy with a standard deviation of %0.3f" % (scores.mean(), scores.std()))

[0.8277512  0.82200957 0.8354067  0.8354067  0.82870813]
0.830 accuracy with a standard deviation of 0.005


pred_6 = model_6.predict(X_test)


print('Score:\n',model_6.score(X_test,y_test),'\n')

from sklearn.metrics import confusion_matrix
print('Confusion Matrix:\n',confusion_matrix(y_test,pred_6),'\n')

from sklearn.metrics import classification_report
print('Classification Report:\n',classification_report(y_test,pred_6))

Score:
 0.8428899082568807 

Confusion Matrix:
 [[458  73]
 [ 64 277]] 

Classification Report:
               precision    recall  f1-score   support

           0       0.88      0.86      0.87       531
           1       0.79      0.81      0.80       341

    accuracy                           0.84       872
   macro avg       0.83      0.84      0.84       872
weighted avg       0.84      0.84      0.84       872


model_7 = GaussianNB()
model_7.fit(X_trainValid, y_trainValid)

GaussianNB()

GaussianNB()


from sklearn.model_selection import ShuffleSplit
n_samples = X_trainValid.shape[0]
cv = ShuffleSplit(n_splits=5, test_size=0.3, random_state=101)
scores = cross_val_score(model_7, X_trainValid, y_trainValid, cv=cv)
print(scores)
print("%0.3f accuracy with a standard deviation of %0.3f" % (scores.mean(), scores.std()))

[0.86315789 0.85263158 0.85741627 0.87464115 0.85454545]
0.860 accuracy with a standard deviation of 0.008


pred_7 = model_7.predict(X_test)


print('Score:\n',model_7.score(X_test,y_test),'\n')

from sklearn.metrics import confusion_matrix
print('Confusion Matrix:\n',confusion_matrix(y_test,pred_7),'\n')

from sklearn.metrics import classification_report
print('Classification Report:\n',classification_report(y_test,pred_7))

Score:
 0.841743119266055 

Confusion Matrix:
 [[448  83]
 [ 55 286]] 

Classification Report:
               precision    recall  f1-score   support

           0       0.89      0.84      0.87       531
           1       0.78      0.84      0.81       341

    accuracy                           0.84       872
   macro avg       0.83      0.84      0.84       872
weighted avg       0.85      0.84      0.84       872


model_8 = BernoulliNB(alpha=0.1)
model_8.fit(X_trainValid, y_trainValid)

BernoulliNB(alpha=0.1)

BernoulliNB(alpha=0.1)


from sklearn.model_selection import ShuffleSplit
n_samples = X_trainValid.shape[0]
cv = ShuffleSplit(n_splits=5, test_size=0.3, random_state=101)
scores = cross_val_score(model_8, X_trainValid, y_trainValid, cv=cv)
print(scores)
print("%0.3f accuracy with a standard deviation of %0.3f" % (scores.mean(), scores.std()))

[0.71578947 0.69665072 0.70334928 0.71770335 0.71961722]
0.711 accuracy with a standard deviation of 0.009


pred_8 = model_8.predict(X_test)


print('Score:\n',model_8.score(X_test,y_test),'\n')

from sklearn.metrics import confusion_matrix
print('Confusion Matrix:\n',confusion_matrix(y_test,pred_8),'\n')

from sklearn.metrics import classification_report
print('Classification Report:\n',classification_report(y_test,pred_8))

Score:
 0.7018348623853211 

Confusion Matrix:
 [[428 103]
 [157 184]] 

Classification Report:
               precision    recall  f1-score   support

           0       0.73      0.81      0.77       531
           1       0.64      0.54      0.59       341

    accuracy                           0.70       872
   macro avg       0.69      0.67      0.68       872
weighted avg       0.70      0.70      0.70       872


eclf = VotingClassifier(estimators=[('Random Forest', model_1),
                                    ('Gradient Boosting Classifier', model_3),
                                    ('XGboost Classifier', model_2)]
                        ,voting='hard')
eclf.fit(X_trainValid, y_trainValid)

VotingClassifier(estimators=[('Random Forest',
                              RandomForestClassifier(criterion='entropy',
                                                     n_estimators=150,
                                                     random_state=101)),
                             ('Gradient Boosting Classifier',
                              GradientBoostingClassifier(random_state=101)),
                             ('XGboost Classifier',
                              XGBClassifier(base_score=None, booster=None,
                                            callbacks=None,
                                            colsample_bylevel=None,
                                            colsample_bynode=None,
                                            colsample_bytree=None,
                                            earl...
                                            gpu_id=None, grow_policy=None,
                                            importance_type=None,
                                            interaction_constraints=None,
                                            learning_rate=None, max_bin=None,
                                            max_cat_threshold=None,
                                            max_cat_to_onehot=None,
                                            max_delta_step=None, max_depth=None,
                                            max_leaves=None,
                                            min_child_weight=None, missing=nan,
                                            monotone_constraints=None,
                                            n_estimators=100, n_jobs=None,
                                            num_parallel_tree=None,
                                            predictor=None, random_state=None, ...))])

VotingClassifier(estimators=[('Random Forest',
                              RandomForestClassifier(criterion='entropy',
                                                     n_estimators=150,
                                                     random_state=101)),
                             ('Gradient Boosting Classifier',
                              GradientBoostingClassifier(random_state=101)),
                             ('XGboost Classifier',
                              XGBClassifier(base_score=None, booster=None,
                                            callbacks=None,
                                            colsample_bylevel=None,
                                            colsample_bynode=None,
                                            colsample_bytree=None,
                                            earl...
                                            gpu_id=None, grow_policy=None,
                                            importance_type=None,
                                            interaction_constraints=None,
                                            learning_rate=None, max_bin=None,
                                            max_cat_threshold=None,
                                            max_cat_to_onehot=None,
                                            max_delta_step=None, max_depth=None,
                                            max_leaves=None,
                                            min_child_weight=None, missing=nan,
                                            monotone_constraints=None,
                                            n_estimators=100, n_jobs=None,
                                            num_parallel_tree=None,
                                            predictor=None, random_state=None, ...))])

RandomForestClassifier(criterion='entropy', n_estimators=150, random_state=101)

GradientBoostingClassifier(random_state=101)

XGBClassifier(base_score=None, booster=None, callbacks=None,
              colsample_bylevel=None, colsample_bynode=None,
              colsample_bytree=None, early_stopping_rounds=None,
              enable_categorical=False, eval_metric=None, feature_types=None,
              gamma=None, gpu_id=None, grow_policy=None, importance_type=None,
              interaction_constraints=None, learning_rate=None, max_bin=None,
              max_cat_threshold=None, max_cat_to_onehot=None,
              max_delta_step=None, max_depth=None, max_leaves=None,
              min_child_weight=None, missing=nan, monotone_constraints=None,
              n_estimators=100, n_jobs=None, num_parallel_tree=None,
              predictor=None, random_state=None, ...)


from sklearn.model_selection import ShuffleSplit
n_samples = X_trainValid.shape[0]
cv = ShuffleSplit(n_splits=5, test_size=0.3, random_state=101)
scores = cross_val_score(eclf, X_trainValid, y_trainValid, cv=cv)
print(scores)
print("%0.3f accuracy with a standard deviation of %0.3f" % (scores.mean(), scores.std()))

[0.88229665 0.87559809 0.89282297 0.90143541 0.86602871]
0.884 accuracy with a standard deviation of 0.012


pred_9 = eclf.predict(X_test)


print('Score:\n',eclf.score(X_test,y_test),'\n')

from sklearn.metrics import confusion_matrix
print('Confusion Matrix:\n',confusion_matrix(y_test,pred_9),'\n')

from sklearn.metrics import classification_report
print('Classification Report:\n',classification_report(y_test,pred_9))

Score:
 0.8681192660550459 

Confusion Matrix:
 [[456  75]
 [ 40 301]] 

Classification Report:
               precision    recall  f1-score   support

           0       0.92      0.86      0.89       531
           1       0.80      0.88      0.84       341

    accuracy                           0.87       872
   macro avg       0.86      0.87      0.86       872
weighted avg       0.87      0.87      0.87       872


X = pd.concat([X_train,X_valid,X_test],axis=0)


y = pd.concat([y_train,y_valid,y_test],axis=0)


from sklearn.model_selection import cross_val_score


from sklearn import metrics


from sklearn.model_selection import ShuffleSplit
n_samples = X_train.shape[0]
cv = ShuffleSplit(n_splits=5, test_size=0.3, random_state=101)
scores = cross_val_score(model_1, X, y, cv=cv)
scores

array([0.88293803, 0.88752869, 0.87681714, 0.88523336, 0.87911247])


for train_index, test_index in cv.split(X):
    print("%s %s" % (train_index, test_index))

[3231 3156 3250 ...  599 1361 1547] [2178  877  983 ... 1331 4238 2745]
[ 665 4013  151 ... 1162 4243  865] [3853 1888   59 ... 3003 1878  992]
[ 586 1049  193 ... 4245 2452 1716] [1069 4192 3631 ... 2796 3625  932]
[  61 3467 1251 ...  726 2370 1722] [1063 3773 4015 ... 1295 4253 2452]
[3495 4146 3630 ... 3743 2085 1508] [1438 1269 3025 ... 2906 2159  984]


print("%0.3f accuracy with a standard deviation of %0.3f" % (scores.mean(), scores.std()))

0.882 accuracy with a standard deviation of 0.004


from sklearn.metrics import roc_curve
from sklearn.metrics import auc
from sklearn.metrics import precision_score
from sklearn.metrics import recall_score
from sklearn.metrics import f1_score


MLA = [model_0,model_1,model_2,model_3,model_4,model_5,model_6,model_7,model_8,eclf]


MLA_columns = []
MLA_compare = pd.DataFrame(columns = MLA_columns)

row_index = 0
for alg in MLA:  
    
    predicted = alg.fit(X_trainValid, y_trainValid).predict(X_test)
    fp, tp, th = roc_curve(y_test, predicted)
    MLA_name = alg.__class__.__name__
    MLA_compare.loc[row_index,'MLA used'] = MLA_name
    MLA_compare.loc[row_index, 'Train Accuracy'] = round(alg.score(X_trainValid, y_trainValid), 4)
    MLA_compare.loc[row_index, 'Test Accuracy'] = round(alg.score(X_test, y_test), 4)
    MLA_compare.loc[row_index, 'Precission'] = precision_score(y_test, predicted)
    MLA_compare.loc[row_index, 'Recall'] = recall_score(y_test, predicted)
    MLA_compare.loc[row_index, 'AUC'] = auc(fp, tp)
    MLA_compare.loc[row_index, 'F1-Score'] = f1_score(y_test, predicted)
    
    row_index+=1
    
MLA_compare.sort_values(by = ['Test Accuracy'], ascending = False, inplace = True)
MLA_compare


# Creating plot to show the ROC for all MLA
index = 1
for alg in MLA:
    
    
    predicted = alg.fit(X_trainValid, y_trainValid).predict(X_test)
    fp, tp, th = roc_curve(y_test, predicted)
    roc_auc_mla = auc(fp, tp)
    MLA_name = alg.__class__.__name__
    plt.plot(fp, tp, lw=2, alpha=0.3, label='ROC %s (AUC = %0.2f)'  % (MLA_name, roc_auc_mla))
   
    index+=1

plt.title('ROC Curve comparison')
plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)
plt.plot([0,1],[0,1],'r--')
plt.xlim([0,1])
plt.ylim([0,1])
plt.ylabel('True Positive Rate')
plt.xlabel('False Positive Rate')    
plt.show()

	summary_nr	build_stor	nature_of_inj	part_of_body	event_type	evn_factor	hum_factor	task_assigned	fat_cause	fall_ht
count	4.847000e+03	4847.000000	4847.000000	4847.000000	4847.000000	4847.000000	4847.000000	4847.000000	4847.000000	4847.0
mean	2.208956e+08	0.447287	11.852486	13.629255	5.193728	11.983701	9.456984	1.371157	3.065608	0.0
std	5.585438e+04	3.011749	7.634332	7.838927	4.608330	6.198560	6.161380	0.483164	7.247627	0.0
min	2.207779e+08	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	1.000000	0.000000	0.0
25%	2.208494e+08	0.000000	5.000000	10.000000	2.000000	7.000000	1.000000	1.000000	0.000000	0.0
50%	2.209189e+08	0.000000	12.000000	13.000000	5.000000	13.000000	13.000000	1.000000	0.000000	0.0
75%	2.209406e+08	0.000000	21.000000	19.000000	6.000000	18.000000	14.000000	2.000000	0.000000	0.0
max	2.209923e+08	139.000000	22.000000	31.000000	14.000000	18.000000	20.000000	2.000000	30.000000	0.0

	support	itemsets
2	0.873249	(Project Cost_ )
1	0.851435	(Building Stories_ )
9	0.814007	(Project Cost_ , Building Stories_ )
0	0.780941	(Construction End Use_ )
7	0.779564	(Project Cost_ , Construction End Use_ )
6	0.773823	(Building Stories_ , Construction End Use_ )
15	0.772675	(Project Cost_ , Building Stories_ , Construct...
3	0.769460	(Project Type_ )
12	0.769001	(Project Cost_ , Project Type_ )
8	0.769001	(Construction End Use_ , Project Type_ )
17	0.768772	(Project Cost_ , Construction End Use_ , Proje...
10	0.766246	(Building Stories_ , Project Type_ )
16	0.766016	(Building Stories_ , Construction End Use_ , P...
18	0.766016	(Project Cost_ , Building Stories_ , Project T...
20	0.765786	(Project Cost_ , Building Stories_ , Construct...
5	0.649139	(Task Assigned_Regularly Assigned)
4	0.594719	(Degree of Injury_Fatal)
14	0.559357	(Project Cost_ , Task Assigned_Regularly Assig...
11	0.544891	(Building Stories_ , Task Assigned_Regularly A...
19	0.518025	(Project Cost_ , Building Stories_ , Task Assi...
13	0.501722	(Project Cost_ , Degree of Injury_Fatal)

	antecedents	consequents	antecedent support	consequent support	support	confidence	lift	leverage	conviction	zhangs_metric
0	(Building Stories_ )	(Construction End Use_ )	0.851435	0.780941	0.773823	0.908846	1.163782	0.108902	2.403163	0.947281
1	(Construction End Use_ )	(Building Stories_ )	0.780941	0.851435	0.773823	0.990885	1.163782	0.108902	16.299004	0.642443
2	(Project Cost_ )	(Construction End Use_ )	0.873249	0.780941	0.779564	0.892716	1.143128	0.097607	2.041862	0.987824
3	(Construction End Use_ )	(Project Cost_ )	0.780941	0.873249	0.779564	0.998236	1.143128	0.097607	71.846613	0.571571
4	(Construction End Use_ )	(Project Type_ )	0.780941	0.769460	0.769001	0.984710	1.279741	0.168098	15.078177	0.997871
5	(Project Type_ )	(Construction End Use_ )	0.769460	0.780941	0.769001	0.999403	1.279741	0.168098	367.032606	0.948176
6	(Project Cost_ )	(Building Stories_ )	0.873249	0.851435	0.814007	0.932159	1.094809	0.070492	2.189892	0.683219
7	(Building Stories_ )	(Project Cost_ )	0.851435	0.873249	0.814007	0.956041	1.094809	0.070492	2.883388	0.582901
8	(Building Stories_ )	(Project Type_ )	0.851435	0.769460	0.766246	0.899946	1.169581	0.111100	2.304153	0.975956
9	(Project Type_ )	(Building Stories_ )	0.769460	0.851435	0.766246	0.995822	1.169581	0.111100	35.560062	0.628928
10	(Building Stories_ )	(Task Assigned_Regularly Assigned)	0.851435	0.649139	0.544891	0.639968	0.985872	-0.007809	0.974527	-0.087976
11	(Task Assigned_Regularly Assigned)	(Building Stories_ )	0.649139	0.851435	0.544891	0.839406	0.985872	-0.007809	0.925094	-0.039242
12	(Project Cost_ )	(Project Type_ )	0.873249	0.769460	0.769001	0.880621	1.144465	0.097071	1.931150	0.995886
13	(Project Type_ )	(Project Cost_ )	0.769460	0.873249	0.769001	0.999403	1.144465	0.097071	212.371068	0.547539
14	(Project Cost_ )	(Degree of Injury_Fatal)	0.873249	0.594719	0.501722	0.574546	0.966081	-0.017615	0.952586	-0.216915
15	(Degree of Injury_Fatal)	(Project Cost_ )	0.594719	0.873249	0.501722	0.843629	0.966081	-0.017615	0.810580	-0.079724
16	(Project Cost_ )	(Task Assigned_Regularly Assigned)	0.873249	0.649139	0.559357	0.640547	0.986764	-0.007503	0.976097	-0.095698
17	(Task Assigned_Regularly Assigned)	(Project Cost_ )	0.649139	0.873249	0.559357	0.861691	0.986764	-0.007503	0.916431	-0.036823
18	(Project Cost_ , Building Stories_ )	(Construction End Use_ )	0.814007	0.780941	0.772675	0.949224	1.215487	0.136983	4.314237	0.953178
19	(Project Cost_ , Construction End Use_ )	(Building Stories_ )	0.779564	0.851435	0.772675	0.991163	1.164109	0.108927	16.812591	0.639523
20	(Building Stories_ , Construction End Use_ )	(Project Cost_ )	0.773823	0.873249	0.772675	0.998516	1.143450	0.096935	85.430080	0.554669
21	(Project Cost_ )	(Building Stories_ , Construction End Use_ )	0.873249	0.773823	0.772675	0.884828	1.143450	0.096935	1.963814	0.989763
22	(Building Stories_ )	(Project Cost_ , Construction End Use_ )	0.851435	0.779564	0.772675	0.907497	1.164109	0.108927	2.383025	0.948906
23	(Construction End Use_ )	(Project Cost_ , Building Stories_ )	0.780941	0.814007	0.772675	0.989415	1.215487	0.136983	17.571183	0.809302
24	(Building Stories_ , Construction End Use_ )	(Project Type_ )	0.773823	0.769460	0.766016	0.989911	1.286500	0.170590	22.850544	0.984616
25	(Building Stories_ , Project Type_ )	(Construction End Use_ )	0.766246	0.780941	0.766016	0.999700	1.280122	0.167623	730.998393	0.936130
26	(Construction End Use_ , Project Type_ )	(Building Stories_ )	0.769001	0.851435	0.766016	0.996118	1.169929	0.111261	38.272596	0.628778
27	(Building Stories_ )	(Construction End Use_ , Project Type_ )	0.851435	0.769001	0.766016	0.899676	1.169929	0.111261	2.302537	0.977667
28	(Construction End Use_ )	(Building Stories_ , Project Type_ )	0.780941	0.766246	0.766016	0.980888	1.280122	0.167623	12.230745	0.998931
29	(Project Type_ )	(Building Stories_ , Construction End Use_ )	0.769460	0.773823	0.766016	0.995524	1.286500	0.170590	50.527899	0.965983

	antecedents	consequents	antecedent support	consequent support	support	confidence	lift	leverage	conviction	zhangs_metric
61	(Project Type_ )	(Project Cost_ , Building Stories_ , Construct...	0.769460	0.772675	0.765786	0.995225	1.288026	0.171244	47.610362	0.969976
48	(Project Cost_ , Building Stories_ , Construct...	(Project Type_ )	0.772675	0.769460	0.765786	0.991085	1.288026	0.171244	25.858860	0.983693
54	(Project Cost_ , Project Type_ )	(Building Stories_ , Construction End Use_ )	0.769001	0.773823	0.765786	0.995820	1.286883	0.170716	54.104724	0.965063
55	(Building Stories_ , Construction End Use_ )	(Project Cost_ , Project Type_ )	0.773823	0.769001	0.765786	0.989614	1.286883	0.170716	22.241889	0.985638
29	(Project Type_ )	(Building Stories_ , Construction End Use_ )	0.769460	0.773823	0.766016	0.995524	1.286500	0.170590	50.527899	0.965983
24	(Building Stories_ , Construction End Use_ )	(Project Type_ )	0.773823	0.769460	0.766016	0.989911	1.286500	0.170590	22.850544	0.984616
53	(Project Cost_ , Construction End Use_ )	(Building Stories_ , Project Type_ )	0.779564	0.766246	0.765786	0.982327	1.282000	0.168449	13.226598	0.997879
56	(Building Stories_ , Project Type_ )	(Project Cost_ , Construction End Use_ )	0.766246	0.779564	0.765786	0.999401	1.282000	0.168449	367.797933	0.941026
30	(Project Cost_ , Construction End Use_ )	(Project Type_ )	0.779564	0.769460	0.768772	0.986156	1.281620	0.168928	16.652808	0.996831
35	(Project Type_ )	(Project Cost_ , Construction End Use_ )	0.769460	0.779564	0.768772	0.999105	1.281620	0.168928	246.227325	0.953145
31	(Project Cost_ , Project Type_ )	(Construction End Use_ )	0.769001	0.780941	0.768772	0.999701	1.280123	0.168227	733.627095	0.947300
34	(Construction End Use_ )	(Project Cost_ , Project Type_ )	0.780941	0.769001	0.768772	0.984416	1.280123	0.168227	14.823153	0.998935
28	(Construction End Use_ )	(Building Stories_ , Project Type_ )	0.780941	0.766246	0.766016	0.980888	1.280122	0.167623	12.230745	0.998931
25	(Building Stories_ , Project Type_ )	(Construction End Use_ )	0.766246	0.780941	0.766016	0.999700	1.280122	0.167623	730.998393	0.936130
49	(Project Cost_ , Building Stories_ , Project T...	(Construction End Use_ )	0.766016	0.780941	0.765786	0.999700	1.280122	0.167573	730.779334	0.935211
60	(Construction End Use_ )	(Project Cost_ , Building Stories_ , Project T...	0.780941	0.766016	0.765786	0.980594	1.280122	0.167573	12.057263	0.998931
5	(Project Type_ )	(Construction End Use_ )	0.769460	0.780941	0.769001	0.999403	1.279741	0.168098	367.032606	0.948176
4	(Construction End Use_ )	(Project Type_ )	0.780941	0.769460	0.769001	0.984710	1.279741	0.168098	15.078177	0.997871
52	(Project Cost_ , Building Stories_ )	(Construction End Use_ , Project Type_ )	0.814007	0.769001	0.765786	0.940762	1.223355	0.139814	3.899481	0.981628
57	(Construction End Use_ , Project Type_ )	(Project Cost_ , Building Stories_ )	0.769001	0.814007	0.765786	0.995820	1.223355	0.139814	44.492209	0.790376
36	(Project Cost_ , Building Stories_ )	(Project Type_ )	0.814007	0.769460	0.766016	0.941044	1.222992	0.139670	3.910349	0.980321
41	(Project Type_ )	(Project Cost_ , Building Stories_ )	0.769460	0.814007	0.766016	0.995524	1.222992	0.139670	41.550861	0.790897
18	(Project Cost_ , Building Stories_ )	(Construction End Use_ )	0.814007	0.780941	0.772675	0.949224	1.215487	0.136983	4.314237	0.953178
23	(Construction End Use_ )	(Project Cost_ , Building Stories_ )	0.780941	0.814007	0.772675	0.989415	1.215487	0.136983	17.571183	0.809302
27	(Building Stories_ )	(Construction End Use_ , Project Type_ )	0.851435	0.769001	0.766016	0.899676	1.169929	0.111261	2.302537	0.977667
26	(Construction End Use_ , Project Type_ )	(Building Stories_ )	0.769001	0.851435	0.766016	0.996118	1.169929	0.111261	38.272596	0.628778
40	(Building Stories_ )	(Project Cost_ , Project Type_ )	0.851435	0.769001	0.766016	0.899676	1.169929	0.111261	2.302537	0.977667
37	(Project Cost_ , Project Type_ )	(Building Stories_ )	0.769001	0.851435	0.766016	0.996118	1.169929	0.111261	38.272596	0.628778
50	(Project Cost_ , Construction End Use_ , Proje...	(Building Stories_ )	0.768772	0.851435	0.765786	0.996117	1.169927	0.111227	38.261168	0.628149
59	(Building Stories_ )	(Project Cost_ , Construction End Use_ , Proje...	0.851435	0.768772	0.765786	0.899407	1.169927	0.111227	2.298647	0.977660

	NOI_1	NOI_2	NOI_3	POB_1	POB_2	POB_3	POB_4	ET_1	ET_2	ET_3	...	HF_3	EDOW_1	EDOW_2	EDOW_3	Event Day	Event Month	Event Year	Hour	Minutes	Degree of Injury_Nonfatal
0	-0.561173	-0.387281	0.766224	-0.277796	1.26860	-0.584156	-0.640189	0.281613	-0.168331	-0.219396	...	-0.395474	-0.533181	0.982924	-0.608217	-0.648730	0.841180	0.913011	-0.687762	-1.178332	0.735189
1	1.781981	-0.387281	-1.305101	-0.277796	1.26860	-0.584156	-0.640189	0.281613	-0.168331	-0.219396	...	-0.395474	1.875536	-1.017372	-0.608217	0.177358	0.550946	0.913011	-0.687762	1.411746	0.735189
2	1.781981	-0.387281	-1.305101	3.599769	-0.78827	-0.584156	-0.640189	0.281613	-0.168331	-0.219396	...	-0.395474	-0.533181	0.982924	-0.608217	1.711521	0.260711	0.913011	-1.150520	0.548387	-1.360195
3	-0.561173	-0.387281	0.766224	-0.277796	-0.78827	1.711872	-0.640189	0.281613	-0.168331	-0.219396	...	-0.395474	-0.533181	0.982924	-0.608217	1.711521	0.260711	0.913011	0.469134	-1.178332	0.735189
4	1.781981	-0.387281	-1.305101	-0.277796	-0.78827	-0.584156	1.562040	0.281613	-0.168331	-0.219396	...	-0.395474	-0.533181	0.982924	-0.608217	0.885433	0.260711	0.913011	0.006376	-0.027186	0.735189
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
4350	-0.561173	2.582103	-1.305101	-0.277796	1.26860	-0.584156	-0.640189	0.281613	-0.168331	-0.219396	...	-0.395474	-0.533181	-1.017372	1.644151	-1.710843	0.550946	-1.799242	-0.687762	-0.314973	0.735189
4351	-0.561173	-0.387281	0.766224	-0.277796	-0.78827	1.711872	-0.640189	0.281613	-0.168331	-0.219396	...	-0.395474	-0.533181	-1.017372	1.644151	-1.710843	0.550946	-1.799242	-0.919141	0.548387	-1.360195
4352	-0.561173	-0.387281	0.766224	-0.277796	1.26860	-0.584156	-0.640189	0.281613	-0.168331	-0.219396	...	-0.395474	-0.533181	-1.017372	1.644151	-1.710843	0.550946	-1.799242	0.700513	-1.178332	0.735189
4353	-0.561173	-0.387281	0.766224	-0.277796	1.26860	-0.584156	-0.640189	0.281613	-0.168331	-0.219396	...	-0.395474	-0.533181	-1.017372	1.644151	-1.710843	0.550946	-1.799242	0.469134	0.548387	0.735189
4354	-0.561173	-0.387281	0.766224	-0.277796	1.26860	-0.584156	-0.640189	0.281613	-0.168331	-0.219396	...	2.528614	-0.533181	-1.017372	1.644151	-1.710843	0.550946	-1.799242	0.469134	-0.314973	0.735189

	summary_nr	Event Date	Abstract Text	Event Description	Event Keywords	con_end	Construction End Use	build_stor	Building Stories	proj_cost	...	Event type	evn_factor	Environmental Factor	hum_factor	Human Factor	task_assigned	Task Assigned	fat_cause
0	220982664	8/10/2017	At 9:00 a.m. on August 10, 2017, an employee w...	EMPLOYEE'S FINGERS AMPUTATED WHILE OPERATING A...	FINGER,MECHANICAL POWER PRESS,AMPUTATION,GUARD	0		0		0	...	Caught in or between	2	Catch Point/Puncture Action	14	Other	1	Regularly Assigned	0
1	220973937	7/17/2017	At 9:45 a.m. on July 17, 2017, an employee was...	EMPLOYEE'S FINGER IS CAUGHT IN DRILL AND IS AM...	CAUGHT IN,DRIVE SHAFT,RESIDENTIAL CONSTRUCTION...	H	Other building	1	1	0	...	Caught in or between	18	Other	10	Position Inappropriate For Task	1	Regularly Assigned	0
2	220967632	6/30/2017	At 7:30 a.m. on June 30, 2017, an employee was...	EMPLOYEE IS HOSPITALIZED AFTER BEING INJURED I...	AMPUTATED,EXPLOSION,FIREWORKS	0		0		0	...	Other	18	Other	4	Malfunction In Securing/Warning Op	2	Not Regularly Assigned	0
3	220969901	6/30/2017	At 2:00 p.m. on June 30, 2017, an employee was...	EMPLOYEE FALLS FROM ROOF AND IS INJURED	RIB,ROOF,HEAD,FALL PROTECTION,FALL,COLLARBONE,...	C	Commercial building	1	1	D	...	Fall (from elevation)	13	Work-Surface/Facility-Layout Condition	14	Other	1	Regularly Assigned	15
4	220977748	6/23/2017	At 12:20 p.m. on June 23, 2017, an employee wa...	EMPLOYEE IS STRUCK BY PLYWOOD WHILE UNLOADING ...	STRUCK BY,TRUCK,BRAIN,NECK,FRACTURE,UNSTABLE LOAD	0		0		0	...	Struck-by	6	Overhead Moving/Falling Object Action	15	Insufficient/Lack/Housekeeping Program	1	Regularly Assigned	0

	summary_nr	Event Date	Abstract Text	Event Description	Event Keywords	...	Event type	evn_factor	Environmental Factor	hum_factor	Human Factor	task_assigned	Task Assigned
4842	220778856	7/1/2015	At approximately 9:15 a.m. on July 1, 2015, Em...	EMPLOYEE FALLS FROM BILLBOARD, SUSTAINS TRAUMA...	FALL,LOST BALANCE,LADDER	...	Fall (from elevation)	18	Other	8	Perception Malfunction Task-Environment	1	Regularly Assigned
4843	220778096	7/1/2015	At approximately 8:30 a.m. on July 1, 2015, Em...	EMPLOYEE FALLS FROM ELEVATION, SUSTAINS HEAD T...	FALL,LOST BALANCE,HEAD	...	Fall (from elevation)	13	Work-Surface/Facility-Layout Condition	6	Equipment Inappropriate For Operation	2	Not Regularly Assigned
4844	220778112	7/1/2015	At 3:00 p.m. on July 1, 2015, an employee was ...	EMPLOYEE FOUND UNRESPONSIVE BY HOME OWNER DURI...	UNCONSCIOUSNESS	...	Other	18	Other	14	Other	1	Regularly Assigned
4845	220777940	7/1/2015	At approximately 2:30 p.m. on July 1, 2015, Em...	EMPLOYEE IS STRUCK AND KILLED BY LOGGING TRUCK...	STRUCK BY,MOTOR VEHICLE	...	Struck-by	13	Work-Surface/Facility-Layout Condition	1	Misjudgment, Hazardous Situation	1	Regularly Assigned
4846	220778195	7/1/2015	At approximately 2:15 p.m. on July 1, 2015, Em...	EMPLOYEE IS STRUCK BY OVERTURNED STANDUP FORKL...	CRUSHING,STRUCK BY,POWERED INDUSTRIAL VEHICLE,...	...	Struck-by	8	Materials Handling Equip./Method	11	Mater-Handling Procedure Inappropriate	1	Regularly Assigned

	summary_nr	Event Date	Abstract Text	Event Description	Event Keywords	con_end	Construction End Use	build_stor	Building Stories	...	Event type	evn_factor	Environmental Factor	hum_factor	Human Factor	task_assigned	Task Assigned	fat_cause
2011	220924369	1/18/2017	At 2:00 p.m. on January 18, 2017, an employee ...	EMPLOYEE FALLS FROM PURLIN AND FRACTURES NECK	STRUCK AGAINST,UNSTABLE SURFACE,FALL,NECK,FALL...	C	Commercial building	1	1	...	Fall (from elevation)	13	Work-Surface/Facility-Layout Condition	14	Other	1	Regularly Assigned	18
3114	220862478	7/26/2016	At 3:47 a.m. on July 26, 2016, an employee wa...	EMPLOYEE IS KILLED BY HEAT STRESS IN A NECTARI...	AGRICULTURE,HEAT EXHAUSTION,HEAT STROKE,LADDER	0		0		...	Other	17	Weather, Earthquake, Etc.	14	Other	1	Regularly Assigned	0
4058	220846596	12/24/2015	At 11:15 a.m. on December 24, 2015, an employe...	EMPLOYEE FALLS FROM A PLATFORM DURING DEMOLITI...	STRUCK BY,DEMOLITION,FALL,STRUCK AGAINST	B	Multi-family dwelling	4	4	...	Struck-by	6	Overhead Moving/Falling Object Action	14	Other	2	Not Regularly Assigned	20
3562	220847552	4/25/2016	At approximately 11:30 p.m. on April 25, 2016,...	EMPLOYEE IS CAUGHT IN MOVING BLADES OF MIXING ...	CAUGHT IN,LOCKOUT/TAGOUT,MACHINE GUARDING,STRU...	0		0		...	Caught in or between	1	Pinch Point Action	1	Misjudgment, Hazardous Situation	1	Regularly Assigned	0
3003	220930614	8/15/2016	At 5:45 p.m. on August 15, 2016, Employee #1 s...	EMPLOYEE FALLS FROM A ROLLING SCAFFOLD AND SUS...	SCAFFOLDING,WRIST,FALL,FRACTURE,HEAD	0		0		...	Fall (from elevation)	18	Other	14	Other	1	Regularly Assigned	0

	summary_nr	Event Date	Abstract Text	Event Description	Event Keywords	...	Event type	evn_factor	Environmental Factor	hum_factor	Human Factor	task_assigned	Task Assigned
63	220957740	5/17/2017	resulting in the employee's head being crushed...	EMPLOYEE IS KILLED WHEN HIS HEAD IS CAUGHT BET...	CAUGHT IN,STUCK,DITCH,EQUIPMENT OPERATOR	...	Caught in or between	13	Work-Surface/Facility-Layout Condition	1	Misjudgment, Hazardous Situation	1	Regularly Assigned
3081	220873897	7/31/2016	crushed between the arms of the skid-steer loa...	EMPLOYEE IS CRUSHED AND KILLED FROM A SKID STE...	SKIDDER,CRUSHED	...	Caught in or between	3	Shear Point Action	10	Position Inappropriate For Task	2	Not Regularly Assigned
3082	220873897	7/31/2016	cage.	EMPLOYEE IS CRUSHED AND KILLED FROM A SKID STE...	SKIDDER,CRUSHED	...	Caught in or between	3	Shear Point Action	10	Position Inappropriate For Task	2	Not Regularly Assigned

	summary_nr	Event Date	Abstract Text	Event Description	Event Keywords	con_end	Construction End Use	build_stor	Building Stories	proj_cost	...	Event type	evn_factor	Environmental Factor	hum_factor	Human Factor	task_assigned	Task Assigned	hazsub	fat_cause	fall_ht
62	220957740	5/17/2017	At 10:25 a.m. on May 17, 2017, an employee was...	EMPLOYEE IS KILLED WHEN HIS HEAD IS CAUGHT BET...	CAUGHT IN,STUCK,DITCH,EQUIPMENT OPERATOR	0		0		0	...	Caught in or between	13	Work-Surface/Facility-Layout Condition	1	Misjudgment, Hazardous Situation	1	Regularly Assigned	0	0	0
63	220957740	5/17/2017	resulting in the employee's head being crushed...	EMPLOYEE IS KILLED WHEN HIS HEAD IS CAUGHT BET...	CAUGHT IN,STUCK,DITCH,EQUIPMENT OPERATOR	0		0		0	...	Caught in or between	13	Work-Surface/Facility-Layout Condition	1	Misjudgment, Hazardous Situation	1	Regularly Assigned	0	0	0

	summary_nr	Event Date	Abstract Text	Event Description	Event Keywords	...	Event type	evn_factor	Environmental Factor	hum_factor	Human Factor	task_assigned	Task Assigned
3080	220873897	7/31/2016	skid-steer loader were unexpectedly actuated a...	EMPLOYEE IS CRUSHED AND KILLED FROM A SKID STE...	SKIDDER,CRUSHED	...	Caught in or between	3	Shear Point Action	10	Position Inappropriate For Task	2	Not Regularly Assigned
3081	220873897	7/31/2016	crushed between the arms of the skid-steer loa...	EMPLOYEE IS CRUSHED AND KILLED FROM A SKID STE...	SKIDDER,CRUSHED	...	Caught in or between	3	Shear Point Action	10	Position Inappropriate For Task	2	Not Regularly Assigned
3082	220873897	7/31/2016	cage.	EMPLOYEE IS CRUSHED AND KILLED FROM A SKID STE...	SKIDDER,CRUSHED	...	Caught in or between	3	Shear Point Action	10	Position Inappropriate For Task	2	Not Regularly Assigned

	summary_nr	Event Date	Abstract Text	Event Description	Event Keywords	con_end	Construction End Use	build_stor	Building Stories	proj_cost	...	Human Factor	task_assigned	Task Assigned	hazsub	fat_cause	fall_ht	Event DayOfWeek	Event Day	Event Month	Event Year
0	220982664	2017-08-10	At 9:00 a.m. on August 10, 2017, an employee w...	EMPLOYEE'S FINGERS AMPUTATED WHILE OPERATING A...	FINGER,MECHANICAL POWER PRESS,AMPUTATION,GUARD	0		0		0	...	Other	1	Regularly Assigned	0	0	0	Thu	10	8	2017
1	220973937	2017-07-17	At 9:45 a.m. on July 17, 2017, an employee was...	EMPLOYEE'S FINGER IS CAUGHT IN DRILL AND IS AM...	CAUGHT IN,DRIVE SHAFT,RESIDENTIAL CONSTRUCTION...	H	Other building	1	1	0	...	Position Inappropriate For Task	1	Regularly Assigned	0	0	0	Mon	17	7	2017
2	220967632	2017-06-30	At 7:30 a.m. on June 30, 2017, an employee was...	EMPLOYEE IS HOSPITALIZED AFTER BEING INJURED I...	AMPUTATED,EXPLOSION,FIREWORKS	0		0		0	...	Malfunction In Securing/Warning Op	2	Not Regularly Assigned	0	0	0	Fri	30	6	2017
3	220969901	2017-06-30	At 2:00 p.m. on June 30, 2017, an employee was...	EMPLOYEE FALLS FROM ROOF AND IS INJURED	RIB,ROOF,HEAD,FALL PROTECTION,FALL,COLLARBONE,...	C	Commercial building	1	1	D	...	Other	1	Regularly Assigned	0	15	0	Fri	30	6	2017
4	220977748	2017-06-23	At 12:20 p.m. on June 23, 2017, an employee wa...	EMPLOYEE IS STRUCK BY PLYWOOD WHILE UNLOADING ...	STRUCK BY,TRUCK,BRAIN,NECK,FRACTURE,UNSTABLE LOAD	0		0		0	...	Insufficient/Lack/Housekeeping Program	1	Regularly Assigned	0	0	0	Fri	23	6	2017
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
4842	220778856	2015-07-01	At approximately 9:15 a.m. on July 1, 2015, Em...	EMPLOYEE FALLS FROM BILLBOARD, SUSTAINS TRAUMA...	FALL,LOST BALANCE,LADDER	0		0		0	...	Perception Malfunction Task-Environment	1	Regularly Assigned	0	0	0	Wed	1	7	2015
4843	220778096	2015-07-01	At approximately 8:30 a.m. on July 1, 2015, Em...	EMPLOYEE FALLS FROM ELEVATION, SUSTAINS HEAD T...	FALL,LOST BALANCE,HEAD	0		0		0	...	Equipment Inappropriate For Operation	2	Not Regularly Assigned	0	0	0	Wed	1	7	2015
4844	220778112	2015-07-01	At 3:00 p.m. on July 1, 2015, an employee was ...	EMPLOYEE FOUND UNRESPONSIVE BY HOME OWNER DURI...	UNCONSCIOUSNESS	0		0		0	...	Other	1	Regularly Assigned	0	0	0	Wed	1	7	2015
4845	220777940	2015-07-01	At approximately 2:30 p.m. on July 1, 2015, Em...	EMPLOYEE IS STRUCK AND KILLED BY LOGGING TRUCK...	STRUCK BY,MOTOR VEHICLE	0		0		0	...	Misjudgment, Hazardous Situation	1	Regularly Assigned	0	0	0	Wed	1	7	2015
4846	220778195	2015-07-01	At approximately 2:15 p.m. on July 1, 2015, Em...	EMPLOYEE IS STRUCK BY OVERTURNED STANDUP FORKL...	CRUSHING,STRUCK BY,POWERED INDUSTRIAL VEHICLE,...	0		0		0	...	Mater-Handling Procedure Inappropriate	1	Regularly Assigned	0	0	0	Wed	1	7	2015

	summary_nr	Event Date	Abstract Text	Event Description	Event Keywords	con_end	Construction End Use	build_stor	Building Stories	proj_cost	...	fat_cause	Event DayOfWeek	Event Day	Event Month	Event Year	Time	Hour	Minutes
153	220956072	2017-04-26	At 6:00 a.m. on April 26, 2017 an employee sta...	EMPLOYEE WORKING IN WAREHOUSE COLLAPSED AND CO...	COLLAPSE	0		0		0	...	0	Wed	26	4	2017	06:00:00	6	0
1149	220947964	2017-03-01	At approximately 8:00 a.m. on March 1, 2017, a...	EMPLOYEE IS EXPOSED TO LEAD DURING PREPARATION...	PAINT,SCRAP METAL,INHALATION,LEAD	C	Commercial building	2	2	E	...	0	Wed	1	3	2017	08:00:00	8	0
1554	220931083	2017-02-08	At 11:15 a.m. on February 8, 2017, three emplo...	THREE EMPLOYEES ARE KILLED IN A TANK EXPLOSION	TANK,ELEVATED WORK PLATFORM,EXPLOSION	D	Manufacturing plant	0		0	...	0	Wed	8	2	2017	11:15:00	11	15
1562	220931760	2017-02-08	At 10:45 a.m. on February 8, 2017, Employee #1...	EMPLOYEE IS IMPALED BY PROBE AND IS HOSPITALIZED	PELVIS,REPAIR,STRUCK AGAINST,WATER PIPE,IMPALE...	G	Sewer/water treatment plant	0		0	...	25	Wed	8	2	2017	10:45:00	10	45
1799	220927982	2017-01-27	At 10:00 a.m. on January 27, 2017, two employe...	TWO EMPLOYEES ARE INJURED IN A CHEMICAL EXPLOSION	CHEMICAL,EXPLOSION,EAR,PRESSURE VESSEL,COMBUST...	0		0		0	...	0	Fri	27	1	2017	10:00:00	10	0
2130	220923262	2017-01-12	At 7:30 a.m. on January 12, 2017, an employee ...	EMPLOYEE IS CAUGHT IN STACKER AND AMPUTATES EAR	STRUCK BY,AMPUTATED,CAUGHT BETWEEN,EAR,MACHINE...	0		0		0	...	0	Thu	12	1	2017	07:30:00	7	30
2378	220913768	2016-12-13	At 10:00 a.m. on December 13, 2016, an employe...	EMPLOYEE IS KILLED WHEN BACKED OVER BY A TRUCK	TIRE,RUN OVER,OIL WELL DRILLING/SERVICING,NATU...	M	Pipeline	0		0	...	7	Tue	13	12	2016	10:00:00	10	0
2858	220885230	2016-09-12	At approximately 2:38 p.m. on September 12, 20...	EMPLOYEE DIED OF HEART ATTACK WHILING TORCHING...	ROOFER,CARDIOVASC SYSTEM,HEART ATTACK	C	Commercial building	1	1	A	...	0	Mon	12	9	2016	14:38:00	14	38
3044	220877328	2016-08-08	At approximately 2:00 p.m. on August 8, 2016, ...	EMPLOYEE IS KILLED IN EXPLOSION WHILE WELDING	WELDER,EXPLOSION	0		0		0	...	0	Mon	8	8	2016	14:00:00	14	0
4246	220813042	2015-11-09	At 9:00 a.m. on November 9, 2015, a worker was...	EMPLOYEE SERVICING TEMPERATURE RELAYS IS ELECT...	ELECTRICAL,ELECTRIC SWITCH,ELECTROCUTED,LOCKOU...	0		0		0	...	0	Mon	9	11	2015	09:00:00	9	0
4420	220798979	2015-09-28	At approximately 9:18 a.m. on September 28, 20...	TWO EMPLOYEES ARE ELECTROCUTED, ONE INJURED WH...	BOOM,ELECTRICAL,ELECTRIC SHOCK,POWER LINES,EXC...	0		0		0	...	0	Mon	28	9	2015	09:18:00	9	18
4797	220785497	2015-07-13	At approximately 12:00 p.m. on July 13, 2015, ...	EMPLOYEE FAINTS AND LATER DIES FROM BLOOD CLOT...	BLOOD CLOT,HEART,FAINTED	0		0		0	...	0	Mon	13	7	2015	12:00:00	12	0

	Event Date	Construction End Use	Building Stories	Project Cost	Project Type	Degree of Injury	Nature of Injury	Part of Body	Event type	Environmental Factor	Human Factor	Task Assigned	Event DayOfWeek	Event Day	Event Month	Event Year	Time	Hour	Minutes
0	2017-08-10					Nonfatal	Amputation, Crushing	Fingers	Caught in or between	Catch Point/Puncture Action	Other	Regularly Assigned	Thu	10	8	2017	09:00:00	9	0
1	2017-07-17	Other building	1		Alteration or rehabilitation	Nonfatal	Dislocation	Fingers	Caught in or between	Other	Position Inappropriate For Task	Regularly Assigned	Mon	17	7	2017	09:45:00	9	45
2	2017-06-30					Nonfatal	Fire Burn	Hand	Other	Other	Malfunction In Securing/Warning Op	Not Regularly Assigned	Fri	30	6	2017	07:30:00	7	30
3	2017-06-30	Commercial building	1	$500,000 to $1,000,000	Alteration or rehabilitation	Fatal	Serious Fall/Strike	Head	Fall (from elevation)	Work-Surface/Facility-Layout Condition	Other	Regularly Assigned	Fri	30	6	2017	14:00:00	14	0
4	2017-06-23					Nonfatal	Bruising, Contusion	Neck	Struck-by	Overhead Moving/Falling Object Action	Insufficient/Lack/Housekeeping Program	Regularly Assigned	Fri	23	6	2017	12:20:00	12	20
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
4842	2015-07-01					Fatal	Head Trauma	Whole Body	Fall (from elevation)	Other	Perception Malfunction Task-Environment	Regularly Assigned	Wed	1	7	2015	09:15:00	9	15
4843	2015-07-01					Fatal	Serious Fall/Strike	Head	Fall (from elevation)	Work-Surface/Facility-Layout Condition	Equipment Inappropriate For Operation	Not Regularly Assigned	Wed	1	7	2015	08:30:00	8	30
4844	2015-07-01					Fatal	Serious Fall/Strike	Whole Body	Other	Other	Other	Regularly Assigned	Wed	1	7	2015	15:00:00	15	0
4845	2015-07-01					Fatal	Serious Fall/Strike	Whole Body	Struck-by	Work-Surface/Facility-Layout Condition	Misjudgment, Hazardous Situation	Regularly Assigned	Wed	1	7	2015	14:30:00	14	30
4846	2015-07-01					Fatal	Serious Fall/Strike	Left Leg	Struck-by	Materials Handling Equip./Method	Mater-Handling Procedure Inappropriate	Regularly Assigned	Wed	1	7	2015	14:15:00	14	15

	NOI_1	NOI_2	NOI_3	POB_1	POB_2	POB_3	POB_4	ET_1	ET_2	ET_3	...	HF_3	EDOW_1	EDOW_2	EDOW_3	Event Day	Event Month	Event Year	Hour	Minutes	Task Assigned_Regularly Assigned
3567	0	0	1	0	0	1	0	1	0	0	...	0	0	0	1	23	4	2016	7	15	0
3483	0	0	1	0	1	0	0	1	0	0	...	1	0	0	1	11	5	2016	8	15	0
3588	1	0	0	0	0	1	0	1	0	0	...	1	1	0	0	18	4	2016	8	30	1
335	1	0	0	0	0	1	0	1	0	0	...	0	0	1	0	14	4	2017	7	30	1
2910	0	1	0	0	0	1	0	1	0	0	...	0	0	1	0	1	9	2016	17	0	0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
4549	0	1	0	0	1	0	0	1	0	0	...	0	0	1	0	1	9	2015	9	40	0
4649	1	0	0	0	0	0	1	1	0	0	...	0	0	1	0	13	8	2015	15	45	1
665	1	0	0	0	0	0	1	1	0	0	...	0	1	0	0	27	3	2017	16	0	1
1472	0	0	1	0	0	1	0	1	0	0	...	0	1	0	0	12	2	2017	18	45	1
1681	0	1	0	0	0	1	0	1	0	0	...	0	0	1	0	2	2	2017	9	15	1

	Value
Event Year	0.559517
Event Month	0.104365
Event Day	0.067043
Hour	0.052529
Minutes	0.048114
POB_3	0.019579
HF_1	0.012186
Task Assigned_Regularly Assigned	0.012071
EDOW_2	0.011526
NOI_3	0.011102
EDOW_1	0.010508
EF_1	0.010396
HF_2	0.008668
EDOW_3	0.008298
NOI_2	0.007591
HF_3	0.007360
EF_2	0.007110
POB_1	0.007106
EF_3	0.006522
ET_2	0.006442
NOI_1	0.006380
POB_2	0.005234
POB_4	0.004648
ET_3	0.004481
ET_1	0.001224

	Value
Event Year	0.339030
Event Month	0.221358
Event Day	0.068683
Hour	0.067433
Minutes	0.046016
POB_3	0.028938
POB_1	0.022458
EF_1	0.021222
EF_2	0.019803
POB_2	0.016934
Task Assigned_Regularly Assigned	0.016564
HF_1	0.012465
NOI_3	0.011770
EDOW_2	0.011597
NOI_2	0.011310
HF_2	0.011056
EDOW_1	0.010707
POB_4	0.010488
EDOW_3	0.009699
NOI_1	0.009484
EF_3	0.007982
ET_1	0.007119
ET_2	0.007045
HF_3	0.006145
ET_3	0.004694

	NOI_1	NOI_2	NOI_3	POB_1	POB_2	POB_3	POB_4	ET_1	ET_2	ET_3	...	HF_3	EDOW_1	EDOW_2	EDOW_3	Event Day	Event Month	Event Year	Hour	Minutes	Task Assigned_Regularly Assigned
3857	0	0	1	0	1	0	0	1	0	0	...	0	0	1	0	18	2	2016	9	30	0
3011	0	1	0	0	0	0	1	1	0	0	...	0	1	0	0	15	8	2016	13	30	1
1838	0	0	1	0	1	0	0	1	0	0	...	1	0	1	0	26	1	2017	14	0	1
1824	0	0	1	0	0	0	1	1	0	0	...	1	0	1	0	26	1	2017	12	45	0
2283	1	0	0	0	1	0	0	1	0	0	...	0	0	1	0	3	1	2017	10	20	1
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
2241	0	0	1	0	1	0	0	1	0	0	...	0	0	1	0	5	1	2017	11	15	0
2258	0	0	1	1	0	0	0	1	0	0	...	0	0	0	1	4	1	2017	12	15	1
2047	0	1	0	0	0	1	0	1	0	0	...	0	0	1	0	17	1	2017	15	30	0
72	0	1	0	0	0	0	1	1	0	0	...	0	0	1	0	16	5	2017	17	15	0
2116	1	0	0	0	0	1	0	1	0	0	...	0	0	1	0	12	1	2017	10	45	1

	Label	Prediction
1749	0	1
450	0	1
92	0	1
1089	1	0
128	0	1
1973	0	1
1356	1	0
81	0	1
620	0	1
818	0	1
244	0	1
85	0	1
1372	1	0
234	0	1
2204	0	1
755	1	0
980	0	1
579	0	1
1011	0	1
1174	1	0
2270	0	1
3352	1	0
1035	1	0
617	1	0
2733	1	0
94	0	1
800	0	1
76	0	1
4771	1	0
2866	1	0
1792	1	0
1624	0	1
283	0	1
1391	0	1
822	0	1
1726	0	1
2282	0	1
96	0	1
65	0	1
1075	0	1
1052	0	1
808	0	1
2319	1	0
91	0	1
2845	1	0
552	0	1
1370	0	1
33	0	1
2522	1	0
2076	0	1
1657	0	1
2616	1	0
2931	1	0
56	0	1
1678	0	1
558	0	1
1898	0	1
595	1	0
72	0	1
2116	0	1

	MLA used	Train Accuracy	Test Accuracy	Precission	Recall	AUC	F1-Score
1	RandomForestClassifier	1.0000	0.8750	0.815217	0.879765	0.875853	0.846262
3	GradientBoostingClassifier	0.9061	0.8693	0.796345	0.894428	0.873768	0.842541
9	VotingClassifier	0.9928	0.8681	0.800532	0.882698	0.870728	0.839609
2	XGBClassifier	0.9917	0.8647	0.814085	0.847507	0.861607	0.830460
4	BaggingClassifier	0.9920	0.8544	0.809249	0.821114	0.848410	0.815138
6	DecisionTreeClassifier	1.0000	0.8429	0.791429	0.812317	0.837420	0.801737
7	GaussianNB	0.8648	0.8417	0.775068	0.838710	0.841200	0.805634
5	SVC	0.8946	0.8394	0.754430	0.873900	0.845613	0.809783
0	LogisticRegression	0.7921	0.7729	0.694823	0.747801	0.768439	0.720339
8	BernoulliNB	0.7126	0.7018	0.641115	0.539589	0.672808	0.585987

Context¶

Import Libraries For Overview & EDA¶

Get The Dataset¶

Overview Of The Dataset¶

Data Cleaning¶

Columns¶

summary_nr¶

Event Date¶

Abstract Text¶

Event Description¶

Event Keywords¶

Construction End Use¶

Building Stories¶

Project Cost¶

Project Type¶

Degree of Injury¶

Nature of Injury¶

Part of Body¶

Event type¶

Environmental Factor¶

Human Factor¶

Task Assigned¶

hazsub¶

fat_cause¶

fall_ht¶

Drop Non-Important Columns¶

EDA: Exploratory Data Analysis¶

Auto EDA with Sweetviz¶

Analyzing the Dataset with Sweetviz¶

Comparing the Outcomes of Target Feature (Fatal & NonFatal)¶

Association Rule Mining: Apriori¶

Build Dataset For Predicting¶

Hash Encoding¶

Train Valid Test Split¶

Predictions¶

Logistic Regression¶

K Nearest Neighbors¶

Decision Tree¶

Hyper Parameter For Decision Tree¶

Random Forest¶

Hyper Parameter For Random Forest¶

Support Vector Machine¶

Hyper Parameter For Support Vector Machine Classification¶

Gaussian Naive Bayes¶

Bernoulli Naive Bayes¶

Hyper Paramets For Bernoulli Naive Bayes¶

Bagging Classifier Algorithm¶

Gradient Boosting Classifier Algorithm¶

XGboost Classifier¶

Voting Classifier¶

Test Models¶

0. Logistic Regression¶

1. Random Forest Classifier¶

2. XGboost Classifier¶

3. Gradient Boosting Classifier¶

4. Bagging Classifier¶

5. Support Vector Machine¶

6. Decision Tree Classifier¶

7. Gaussian Naive Bayes¶

8. Bernoulli Naive Bayes¶

9. Voting Classifier¶

Cross Validation: Evaluating Estimator Performance¶

Comparing Machine Learning Algorithms¶

END =)¶